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important update of the repository.
Jupyter notebook shows how to use the toolbox.,
Kilian Vos 6 years ago
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*.pyc
*.mat
*.tif
*.png
*.mp4
*.gif
*.jpg
*.pkl

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.ipynb_checkpoints/shoreline_extraction-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Shoreline extraction from satellite images"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook shows how to download satellite images (Landsat 5,7,8 and Sentinel-2) from Google Earth Engine and apply the shoreline detection algorithm described in *Vos K., Harley M.D., Splinter K.D., Simmons J.A., Turner I.L. (in review). Capturing intra-annual to multi-decadal shoreline variability from publicly available satellite imagery, Coastal Engineering*. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initial settings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Python packages required to run this notebook can be installed by running the following anaconda command:\n",
"*\"conda env create -f environment.yml\"*. This will create a new enviroment with all the relevant packages installed. You will also need to sign up for Google Earth Engine (https://earthengine.google.com and go to signup) and authenticate on the computer so that python can access via your login."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pickle\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"# load modules from directory\n",
"import SDS_download, SDS_preprocess, SDS_tools, SDS_shoreline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download images"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the region of interest, the dates and the satellite missions from which you want to download images. The image will be cropped on the Google Earth Engine server and only the region of interest will be downloaded resulting in low memory allocation (~ 1 megabyte/image for 5 km of beach)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# define the area of interest (longitude, latitude)\n",
"polygon = [[[151.301454, -33.700754],\n",
" [151.311453, -33.702075], \n",
" [151.307237, -33.739761],\n",
" [151.294220, -33.736329],\n",
" [151.301454, -33.700754]]]\n",
" \n",
"# define dates of interest\n",
"dates = ['2017-12-01','2018-01-01']\n",
"\n",
"# define satellite missions ('L5' --> landsat 5 , 'S2' --> Sentinel-2)\n",
"sat_list = ['L5', 'L7', 'L8', 'S2']\n",
"\n",
"# give a name to the site\n",
"sitename = 'NARRA'\n",
"\n",
"# download satellite images. The cropped images are saved in a '/data' subfolder. The image information is stored\n",
"# into 'metadata.pkl'.\n",
"# SDS_download.get_images(sitename, polygon, dates, sat_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Shoreline extraction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Performs a sub-pixel resolution shoreline detection method integrating a supervised classification component that allows to map the boundary between water and sand."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# parameters and settings\n",
"%matplotlib qt\n",
"settings = { \n",
" 'sitename': sitename,\n",
" \n",
" # general parameters:\n",
" 'cloud_thresh': 0.5, # threshold on maximum cloud cover\n",
" 'output_epsg': 28356, # epsg code of the desired output spatial reference system\n",
" \n",
" # shoreline detection parameters:\n",
" 'min_beach_size': 20, # minimum number of connected pixels for a beach\n",
" 'buffer_size': 7, # radius (in pixels) of disk for buffer around sandy pixels\n",
" 'min_length_sl': 200, # minimum length of shoreline perimeter to be kept \n",
" 'max_dist_ref': 100 , # max distance (in meters) allowed from a reference shoreline\n",
" \n",
" # quality control:\n",
" 'check_detection': True # if True, shows each shoreline detection and lets the user \n",
" # decide which shorleines are correct and which ones are false due to\n",
" # the presence of clouds and other artefacts. \n",
" # If set to False, shorelines are extracted from all images.\n",
" }\n",
"\n",
"# load metadata structure (contains information on the downloaded satellite images and is created\n",
"# after all images have been successfully downloaded)\n",
"filepath = os.path.join(os.getcwd(), 'data', settings['sitename'])\n",
"with open(os.path.join(filepath, settings['sitename'] + '_metadata' + '.pkl'), 'rb') as f:\n",
" metadata = pickle.load(f)\n",
" \n",
"# [OPTIONAL] saves .jpg files of the preprocessed images (cloud mask and pansharpening/down-sampling) \n",
"#SDS_preprocess.preprocess_all_images(metadata, settings)\n",
"\n",
"# [OPTIONAL] to avoid false detections and identify obvious outliers there is the option to\n",
"# create a reference shoreline position (manually clicking on a satellite image)\n",
"settings['refsl'] = SDS_preprocess.get_reference_sl(metadata, settings)\n",
"\n",
"# extract shorelines from all images. Saves out.pkl which contains the shoreline coordinates for each date in the spatial\n",
"# reference system specified in settings['output_epsg']. Save the output in a file called 'out.pkl'.\n",
"out = SDS_shoreline.extract_shorelines(metadata, settings)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot the shorelines"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"plt.axis('equal')\n",
"plt.xlabel('Eastings [m]')\n",
"plt.ylabel('Northings [m]')\n",
"plt.title('Shorelines')\n",
"for satname in out.keys():\n",
" if satname == 'meta':\n",
" continue\n",
" for i in range(len(out[satname]['shoreline'])):\n",
" sl = out[satname]['shoreline'][i]\n",
" date = out[satname]['timestamp'][i]\n",
" plt.plot(sl[:,0], sl[:,1], '-', label=date.strftime('%d-%m-%Y'))\n",
"plt.legend()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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33
README.md
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# coastsat
Python code to download publicly available satellite imagery with Google Earth Engine API and extract shorelines using a robust sub-pixel resolution shoreline detection algorithm described in *Vos K., Harley M.D., Splinter K.D., Simmons J.A., Turner I.L. (in review). Capturing intra-annual to multi-decadal shoreline variability from publicly available satellite imagery, Coastal Engineering*.
Written by *Kilian Vos*.
## Description
Satellite remote sensing can provide low-cost long-term shoreline data capable of resolving the temporal scales of interest to coastal scientists and engineers at sites where no in-situ measurements are available. Satellite imagery spannig the last 30 years with constant revisit periods is publicly available and suitable to extract repeated measurements of the shoreline positon.
*coastsat* is an open-source Python module that allows to extract shorelines from Landsat 5, Landsat 7, Landsat 8 and Sentinel-2 images.
The shoreline detection algorithm proposed here combines a sub-pixel border segmentation and an image classification component, which refines the segmentation into four distinct categories such that the shoreline detection is specific to the sand/water interface.
## Use
A demonstration of the use of *coastsat* is provided in the Jupyter Notebook *shoreline_extraction.ipynb*. The code can also be run in Spyder with *main_spyder.py*.
The Python packages required to run this notebook can be installed by running the following anaconda command: *conda env create -f environment.yml*. This will create a new enviroment with all the relevant packages installed. You will also need to sign up for Google Earth Engine (https://earthengine.google.com and go to signup) and authenticate on the computer so that python can access via your login.
The first step is to retrieve the satellite images of the region of interest from Google Earth Engine servers by calling *SDS_download.get_images(sitename, polygon, dates, sat_list)*:
- *sitename* is a string which will define the name of the folder where the files will be stored
- *polygon* contains the coordinates of the region of interest (longitude/latitude pairs)
- *dates* defines the dates over which the images will be retrieved (e.g., *dates = ['2017-12-01', '2018-01-01']*)
- *sat_list* indicates which satellite missions to consider (e.g., *sat_list = ['L5', 'L7', 'L8', 'S2']* will download images from Landsat 5, 7, 8 and Sentinel-2 collections).
The images are cropped on the Google Earth Engine servers and only the region of interest is downloaded resulting in low memory allocation (~ 1 megabyte/image for a 5km-long beach). The relevant image metadata (time of acquisition, geometric accuracy...etc) is stored in a file named *sitename_metadata.pkl*.
Once the images have been downloaded, the shorelines are extracted from the multispectral images using the sub-pixel resolution technique described in *Vos K., Harley M.D., Splinter K.D., Simmons J.A., Turner I.L. (in review). Capturing intra-annual to multi-decadal shoreline variability from publicly available satellite imagery, Coastal Engineering*.
The shoreline extraction is performed by the function SDS_shoreline.extract_shorelines(metadata, settings). The user must define the settings in a Python dictionary. To ensure maximum robustness of the algorithm the user can optionally digitize a reference shoreline (byc calling *SDS_preprocess.get_reference_sl(metadata, settings)*) that will then be used to identify obvious outliers and minimize false detections. Since the cloud mask is not perfect (especially in Sentinel-2 images) the user has the option to manually validate each detection by setting the *'check_detection'* parameter to *True*.
The shoreline coordinates (in the coordinate system defined by the user in *'output_epsg'* are stored in a file named *sitename_out.pkl*.
## Issues and Contributions
Looking to contribute to the code? Please see the [Issues page](https://github.com/kvos/coastsat/issues).

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SDS_download.py
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"""This module contains all the functions needed to download the satellite images from GEE
Author: Kilian Vos, Water Research Laboratory, University of New South Wales
"""
# Initial settings
import os
import numpy as np
import matplotlib.pyplot as plt
import pdb
import ee
from urllib.request import urlretrieve
from datetime import datetime
import pytz
import pickle
import zipfile
# initialise connection with GEE server
ee.Initialize()
# Functions
def download_tif(image, polygon, bandsId, filepath):
"""
Downloads a .TIF image from the ee server and stores it in a temp file
Arguments:
-----------
image: ee.Image
Image object to be downloaded
polygon: list
polygon containing the lon/lat coordinates to be extracted
longitudes in the first column and latitudes in the second column
bandsId: list of dict
list of bands to be downloaded
filepath: location where the temporary file should be saved
"""
url = ee.data.makeDownloadUrl(ee.data.getDownloadId({
'image': image.serialize(),
'region': polygon,
'bands': bandsId,
'filePerBand': 'false',
'name': 'data',
}))
local_zip, headers = urlretrieve(url)
with zipfile.ZipFile(local_zip) as local_zipfile:
return local_zipfile.extract('data.tif', filepath)
def get_images(sitename,polygon,dates,sat):
"""
Downloads all images from Landsat 5, Landsat 7, Landsat 8 and Sentinel-2 covering the given
polygon and acquired during the given dates. The images are organised in subfolders and divided
by satellite mission and pixel resolution.
KV WRL 2018
Arguments:
-----------
sitename: str
String containig the name of the site
polygon: list
polygon containing the lon/lat coordinates to be extracted
longitudes in the first column and latitudes in the second column
dates: list of str
list that contains 2 strings with the initial and final dates in format 'yyyy-mm-dd'
e.g. ['1987-01-01', '2018-01-01']
sat: list of str
list that contains the names of the satellite missions to include
e.g. ['L5', 'L7', 'L8', 'S2']
"""
# format in which the images are downloaded
suffix = '.tif'
# initialise metadata dictionnary (stores timestamps and georefencing accuracy of each image)
metadata = dict([])
# create directories
try:
os.makedirs(os.path.join(os.getcwd(), 'data',sitename))
except:
print('')
#=============================================================================================#
# download L5 images
#=============================================================================================#
if 'L5' in sat or 'Landsat5' in sat:
satname = 'L5'
# create a subfolder to store L5 images
filepath = os.path.join(os.getcwd(), 'data', sitename, satname, '30m')
try:
os.makedirs(filepath)
except:
print('')
# Landsat 5 collection
input_col = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA')
# filter by location and dates
flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(dates[0],dates[1])
# get all images in the filtered collection
im_all = flt_col.getInfo().get('features')
# print how many images there are for the user
n_img = flt_col.size().getInfo()
print('Number of ' + satname + ' images covering ' + sitename + ':', n_img)
# loop trough images
timestamps = []
acc_georef = []
all_names = []
im_epsg = []
for i in range(n_img):
# find each image in ee database
im = ee.Image(im_all[i].get('id'))
# read metadata
im_dic = im.getInfo()
# get bands
im_bands = im_dic.get('bands')
# get time of acquisition (UNIX time)
t = im_dic['properties']['system:time_start']
# convert to datetime
im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
timestamps.append(im_timestamp)
im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S')
# get EPSG code of reference system
im_epsg.append(int(im_dic['bands'][0]['crs'][5:]))
# get geometric accuracy
try:
acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
except:
# default value of accuracy (RMSE = 12m)
acc_georef.append(12)
print('No geometric rmse model property')
# delete dimensions key from dictionnary, otherwise the entire image is extracted
for j in range(len(im_bands)): del im_bands[j]['dimensions']
# bands for L5
ms_bands = [im_bands[0], im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[7]]
# filenames for the images
filename = im_date + '_' + satname + '_' + sitename + suffix
# if two images taken at the same date add 'dup' in the name
if any(filename in _ for _ in all_names):
filename = im_date + '_' + satname + '_' + sitename + '_dup' + suffix
all_names.append(filename)
# download .TIF image
local_data = download_tif(im, polygon, ms_bands, filepath)
# update filename
os.rename(local_data, os.path.join(filepath, filename))
print(i, end='..')
# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
timestamps_sorted = sorted(timestamps)
idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
im_epsg_sorted = [im_epsg[j] for j in idx_sorted]
# save into dict
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted,
'epsg':im_epsg_sorted}
print('Finished with ' + satname)
#=============================================================================================#
# download L7 images
#=============================================================================================#
if 'L7' in sat or 'Landsat7' in sat:
satname = 'L7'
# create subfolders (one for 30m multispectral bands and one for 15m pan bands)
filepath = os.path.join(os.getcwd(), 'data', sitename, 'L7')
filepath_pan = os.path.join(filepath, 'pan')
filepath_ms = os.path.join(filepath, 'ms')
try:
os.makedirs(filepath_pan)
os.makedirs(filepath_ms)
except:
print('')
# landsat 7 collection
input_col = ee.ImageCollection('LANDSAT/LE07/C01/T1_RT_TOA')
# filter by location and dates
flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(dates[0],dates[1])
# get all images in the filtered collection
im_all = flt_col.getInfo().get('features')
# print how many images there are for the user
n_img = flt_col.size().getInfo()
print('Number of ' + satname + ' images covering ' + sitename + ':', n_img)
# loop trough images
timestamps = []
acc_georef = []
all_names = []
im_epsg = []
for i in range(n_img):
# find each image in ee database
im = ee.Image(im_all[i].get('id'))
# read metadata
im_dic = im.getInfo()
# get bands
im_bands = im_dic.get('bands')
# get time of acquisition (UNIX time)
t = im_dic['properties']['system:time_start']
# convert to datetime
im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
timestamps.append(im_timestamp)
im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S')
# get EPSG code of reference system
im_epsg.append(int(im_dic['bands'][0]['crs'][5:]))
# get geometric accuracy
try:
acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
except:
# default value of accuracy (RMSE = 12m)
acc_georef.append(12)
print('No geometric rmse model property')
# delete dimensions key from dictionnary, otherwise the entire image is extracted
for j in range(len(im_bands)): del im_bands[j]['dimensions']
# bands for L7
pan_band = [im_bands[8]]
ms_bands = [im_bands[0], im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[9]]
# filenames for the images
filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + suffix
filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + suffix
# if two images taken at the same date add 'dup' in the name
if any(filename_pan in _ for _ in all_names):
filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + '_dup' + suffix
filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + '_dup' + suffix
all_names.append(filename_pan)
# download .TIF image
local_data_pan = download_tif(im, polygon, pan_band, filepath_pan)
local_data_ms = download_tif(im, polygon, ms_bands, filepath_ms)
# update filename
os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan))
os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms))
print(i, end='..')
# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
timestamps_sorted = sorted(timestamps)
idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
im_epsg_sorted = [im_epsg[j] for j in idx_sorted]
# save into dict
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted,
'epsg':im_epsg_sorted}
print('Finished with ' + satname)
#=============================================================================================#
# download L8 images
#=============================================================================================#
if 'L8' in sat or 'Landsat8' in sat:
satname = 'L8'
# create subfolders (one for 30m multispectral bands and one for 15m pan bands)
filepath = os.path.join(os.getcwd(), 'data', sitename, 'L8')
filepath_pan = os.path.join(filepath, 'pan')
filepath_ms = os.path.join(filepath, 'ms')
try:
os.makedirs(filepath_pan)
os.makedirs(filepath_ms)
except:
print('')
# landsat 8 collection
input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
# filter by location and dates
flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(dates[0],dates[1])
# get all images in the filtered collection
im_all = flt_col.getInfo().get('features')
# print how many images there are for the user
n_img = flt_col.size().getInfo()
print('Number of ' + satname + ' images covering ' + sitename + ':', n_img)
# loop trough images
timestamps = []
acc_georef = []
all_names = []
im_epsg = []
for i in range(n_img):
# find each image in ee database
im = ee.Image(im_all[i].get('id'))
# read metadata
im_dic = im.getInfo()
# get bands
im_bands = im_dic.get('bands')
# get time of acquisition (UNIX time)
t = im_dic['properties']['system:time_start']
# convert to datetime
im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
timestamps.append(im_timestamp)
im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S')
# get EPSG code of reference system
im_epsg.append(int(im_dic['bands'][0]['crs'][5:]))
# get geometric accuracy
try:
acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
except:
# default value of accuracy (RMSE = 12m)
acc_georef.append(12)
print('No geometric rmse model property')
# delete dimensions key from dictionnary, otherwise the entire image is extracted
for j in range(len(im_bands)): del im_bands[j]['dimensions']
# bands for L8
pan_band = [im_bands[7]]
ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5], im_bands[11]]
# filenames for the images
filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + suffix
filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + suffix
# if two images taken at the same date add 'dup' in the name
if any(filename_pan in _ for _ in all_names):
filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + '_dup' + suffix
filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + '_dup' + suffix
all_names.append(filename_pan)
# download .TIF image
local_data_pan = download_tif(im, polygon, pan_band, filepath_pan)
local_data_ms = download_tif(im, polygon, ms_bands, filepath_ms)
# update filename
os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan))
os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms))
print(i, end='..')
# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
timestamps_sorted = sorted(timestamps)
idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
im_epsg_sorted = [im_epsg[j] for j in idx_sorted]
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted,
'epsg':im_epsg_sorted}
print('Finished with ' + satname)
#=============================================================================================#
# download S2 images
#=============================================================================================#
if 'S2' in sat or 'Sentinel2' in sat:
satname = 'S2'
# create subfolders for the 10m, 20m and 60m multipectral bands
filepath = os.path.join(os.getcwd(), 'data', sitename, 'S2')
try:
os.makedirs(os.path.join(filepath, '10m'))
os.makedirs(os.path.join(filepath, '20m'))
os.makedirs(os.path.join(filepath, '60m'))
except:
print('')
# Sentinel2 collection
input_col = ee.ImageCollection('COPERNICUS/S2')
# filter by location and dates
flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(dates[0],dates[1])
# get all images in the filtered collection
im_all = flt_col.getInfo().get('features')
# print how many images there are
n_img = flt_col.size().getInfo()
print('Number of ' + satname + ' images covering ' + sitename + ':', n_img)
# loop trough images
timestamps = []
acc_georef = []
all_names = []
im_epsg = []
for i in range(n_img):
# find each image in ee database
im = ee.Image(im_all[i].get('id'))
# read metadata
im_dic = im.getInfo()
# get bands
im_bands = im_dic.get('bands')
# get time of acquisition (UNIX time)
t = im_dic['properties']['system:time_start']
# convert to datetime
im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S')
# delete dimensions key from dictionnary, otherwise the entire image is extracted
for j in range(len(im_bands)): del im_bands[j]['dimensions']
# bands for S2
bands10 = [im_bands[1], im_bands[2], im_bands[3], im_bands[7]]
bands20 = [im_bands[11]]
bands60 = [im_bands[15]]
# filenames for images
filename10 = im_date + '_' + satname + '_' + sitename + '_' + '10m' + suffix
filename20 = im_date + '_' + satname + '_' + sitename + '_' + '20m' + suffix
filename60 = im_date + '_' + satname + '_' + sitename + '_' + '60m' + suffix
# if two images taken at the same date skip the second image (they are the same)
if any(filename10 in _ for _ in all_names):
continue
all_names.append(filename10)
# download .TIF image and update filename
local_data = download_tif(im, polygon, bands10, os.path.join(filepath, '10m'))
os.rename(local_data, os.path.join(filepath, '10m', filename10))
local_data = download_tif(im, polygon, bands20, os.path.join(filepath, '20m'))
os.rename(local_data, os.path.join(filepath, '20m', filename20))
local_data = download_tif(im, polygon, bands60, os.path.join(filepath, '60m'))
os.rename(local_data, os.path.join(filepath, '60m', filename60))
# save timestamp, epsg code and georeferencing accuracy (1 if passed 0 if not passed)
timestamps.append(im_timestamp)
im_epsg.append(int(im_dic['bands'][0]['crs'][5:]))
try:
if im_dic['properties']['GEOMETRIC_QUALITY_FLAG'] == 'PASSED':
acc_georef.append(1)
else:
acc_georef.append(0)
except:
acc_georef.append(0)
print(i, end='..')
# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
timestamps_sorted = sorted(timestamps)
idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
im_epsg_sorted = [im_epsg[j] for j in idx_sorted]
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted,
'epsg':im_epsg_sorted}
print('Finished with ' + satname)
# save metadata dict
filepath = os.path.join(os.getcwd(), 'data', sitename)
with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'wb') as f:
pickle.dump(metadata, f)

667
SDS_preprocess.py
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"""This module contains all the functions needed to preprocess the satellite images: creating a
cloud mask and pansharpening/downsampling the images.
Author: Kilian Vos, Water Research Laboratory, University of New South Wales
"""
# Initial settings
import os
import numpy as np
import matplotlib.pyplot as plt
from osgeo import gdal, ogr, osr
import skimage.transform as transform
import skimage.morphology as morphology
import sklearn.decomposition as decomposition
import skimage.exposure as exposure
from pylab import ginput
import pickle
import pdb
import SDS_tools
# Functions
def create_cloud_mask(im_qa, satname):
"""
Creates a cloud mask from the image containing the QA band information.
KV WRL 2018
Arguments:
-----------
im_qa: np.array
Image containing the QA band
satname: string
short name for the satellite (L8, L7, S2)
Returns:
-----------
cloud_mask : np.ndarray of booleans
A boolean array with True where the cloud are present
"""
# convert QA bits depending on the satellite mission
if satname == 'L8':
cloud_values = [2800, 2804, 2808, 2812, 6896, 6900, 6904, 6908]
elif satname == 'L7' or satname == 'L5' or satname == 'L4':
cloud_values = [752, 756, 760, 764]
elif satname == 'S2':
cloud_values = [1024, 2048] # 1024 = dense cloud, 2048 = cirrus clouds
# find which pixels have bits corresponding to cloud values
cloud_mask = np.isin(im_qa, cloud_values)
# remove isolated cloud pixels (there are some in the swash zone and they can cause problems)
if sum(sum(cloud_mask)) > 0 and sum(sum(~cloud_mask)) > 0:
morphology.remove_small_objects(cloud_mask, min_size=10, connectivity=1, in_place=True)
return cloud_mask
def hist_match(source, template):
"""
Adjust the pixel values of a grayscale image such that its histogram matches that of a
target image.
Arguments:
-----------
source: np.array
Image to transform; the histogram is computed over the flattened
array
template: np.array
Template image; can have different dimensions to source
Returns:
-----------
matched: np.array
The transformed output image
"""
oldshape = source.shape
source = source.ravel()
template = template.ravel()
# get the set of unique pixel values and their corresponding indices and
# counts
s_values, bin_idx, s_counts = np.unique(source, return_inverse=True,
return_counts=True)
t_values, t_counts = np.unique(template, return_counts=True)
# take the cumsum of the counts and normalize by the number of pixels to
# get the empirical cumulative distribution functions for the source and
# template images (maps pixel value --> quantile)
s_quantiles = np.cumsum(s_counts).astype(np.float64)
s_quantiles /= s_quantiles[-1]
t_quantiles = np.cumsum(t_counts).astype(np.float64)
t_quantiles /= t_quantiles[-1]
# interpolate linearly to find the pixel values in the template image
# that correspond most closely to the quantiles in the source image
interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)
return interp_t_values[bin_idx].reshape(oldshape)
def pansharpen(im_ms, im_pan, cloud_mask):
"""
Pansharpens a multispectral image (3D), using the panchromatic band (2D) and a cloud mask.
A PCA is applied to the image, then the 1st PC is replaced with the panchromatic band.
KV WRL 2018
Arguments:
-----------
im_ms: np.array
Multispectral image to pansharpen (3D)
im_pan: np.array
Panchromatic band (2D)
cloud_mask: np.array
2D cloud mask with True where cloud pixels are
Returns:
-----------
im_ms_ps: np.ndarray
Pansharpened multisoectral image (3D)
"""
# reshape image into vector and apply cloud mask
vec = im_ms.reshape(im_ms.shape[0] * im_ms.shape[1], im_ms.shape[2])
vec_mask = cloud_mask.reshape(im_ms.shape[0] * im_ms.shape[1])
vec = vec[~vec_mask, :]
# apply PCA to RGB bands
pca = decomposition.PCA()
vec_pcs = pca.fit_transform(vec)
# replace 1st PC with pan band (after matching histograms)
vec_pan = im_pan.reshape(im_pan.shape[0] * im_pan.shape[1])
vec_pan = vec_pan[~vec_mask]
vec_pcs[:,0] = hist_match(vec_pan, vec_pcs[:,0])
vec_ms_ps = pca.inverse_transform(vec_pcs)
# reshape vector into image
vec_ms_ps_full = np.ones((len(vec_mask), im_ms.shape[2])) * np.nan
vec_ms_ps_full[~vec_mask,:] = vec_ms_ps
im_ms_ps = vec_ms_ps_full.reshape(im_ms.shape[0], im_ms.shape[1], im_ms.shape[2])
return im_ms_ps
def rescale_image_intensity(im, cloud_mask, prob_high):
"""
Rescales the intensity of an image (multispectral or single band) by applying
a cloud mask and clipping the prob_high upper percentile. This functions allows
to stretch the contrast of an image for visualisation purposes.
KV WRL 2018
Arguments:
-----------
im: np.array
Image to rescale, can be 3D (multispectral) or 2D (single band)
cloud_mask: np.array
2D cloud mask with True where cloud pixels are
prob_high: float
probability of exceedence used to calculate the upper percentile
Returns:
-----------
im_adj: np.array
The rescaled image
"""
# lower percentile is set to 0
prc_low = 0
# reshape the 2D cloud mask into a 1D vector
vec_mask = cloud_mask.reshape(im.shape[0] * im.shape[1])
# if image contains several bands, stretch the contrast for each band
if len(im.shape) > 2:
# reshape into a vector
vec = im.reshape(im.shape[0] * im.shape[1], im.shape[2])
# initiliase with NaN values
vec_adj = np.ones((len(vec_mask), im.shape[2])) * np.nan
# loop through the bands
for i in range(im.shape[2]):
# find the higher percentile (based on prob)
prc_high = np.percentile(vec[~vec_mask, i], prob_high)
# clip the image around the 2 percentiles and rescale the contrast
vec_rescaled = exposure.rescale_intensity(vec[~vec_mask, i],
in_range=(prc_low, prc_high))
vec_adj[~vec_mask,i] = vec_rescaled
# reshape into image
im_adj = vec_adj.reshape(im.shape[0], im.shape[1], im.shape[2])
# if image only has 1 bands (grayscale image)
else:
vec = im.reshape(im.shape[0] * im.shape[1])
vec_adj = np.ones(len(vec_mask)) * np.nan
prc_high = np.percentile(vec[~vec_mask], prob_high)
vec_rescaled = exposure.rescale_intensity(vec[~vec_mask], in_range=(prc_low, prc_high))
vec_adj[~vec_mask] = vec_rescaled
im_adj = vec_adj.reshape(im.shape[0], im.shape[1])
return im_adj
def preprocess_single(fn, satname):
"""
Creates a cloud mask using the QA band and performs pansharpening/down-sampling of the image.
KV WRL 2018
Arguments:
-----------
fn: str or list of str
filename of the .TIF file containing the image
for L7, L8 and S2 there is a filename for the bands at different resolutions
satname: str
name of the satellite mission (e.g., 'L5')
Returns:
-----------
im_ms: np.array
3D array containing the pansharpened/down-sampled bands (B,G,R,NIR,SWIR1)
georef: np.array
vector of 6 elements [Xtr, Xscale, Xshear, Ytr, Yshear, Yscale] defining the
coordinates of the top-left pixel of the image
cloud_mask: np.array
2D cloud mask with True where cloud pixels are
"""
#=============================================================================================#
# L5 images
#=============================================================================================#
if satname == 'L5':
# read all bands
data = gdal.Open(fn, gdal.GA_ReadOnly)
georef = np.array(data.GetGeoTransform())
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
im_ms = np.stack(bands, 2)
# down-sample to 15 m (half of the original pixel size)
nrows = im_ms.shape[0]*2
ncols = im_ms.shape[1]*2
# create cloud mask
im_qa = im_ms[:,:,5]
im_ms = im_ms[:,:,:-1]
cloud_mask = create_cloud_mask(im_qa, satname)
# resize the image using bilinear interpolation (order 1)
im_ms = transform.resize(im_ms,(nrows, ncols), order=1, preserve_range=True,
mode='constant')
# resize the image using nearest neighbour interpolation (order 0)
cloud_mask = transform.resize(cloud_mask, (nrows, ncols), order=0, preserve_range=True,
mode='constant').astype('bool_')
# adjust georeferencing vector to the new image size
# scale becomes 15m and the origin is adjusted to the center of new top left pixel
georef[1] = 15
georef[5] = -15
georef[0] = georef[0] + 7.5
georef[3] = georef[3] - 7.5
# check if -inf or nan values on any band and add to cloud mask
for k in range(im_ms.shape[2]):
im_inf = np.isin(im_ms[:,:,k], -np.inf)
im_nan = np.isnan(im_ms[:,:,k])
cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
# calculate cloud cover
cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
#=============================================================================================#
# L7 images
#=============================================================================================#
elif satname == 'L7':
# read pan image
fn_pan = fn[0]
data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
georef = np.array(data.GetGeoTransform())
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
im_pan = np.stack(bands, 2)[:,:,0]
# size of pan image
nrows = im_pan.shape[0]
ncols = im_pan.shape[1]
# read ms image
fn_ms = fn[1]
data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
im_ms = np.stack(bands, 2)
# create cloud mask
im_qa = im_ms[:,:,5]
cloud_mask = create_cloud_mask(im_qa, satname)
# resize the image using bilinear interpolation (order 1)
im_ms = im_ms[:,:,:5]
im_ms = transform.resize(im_ms,(nrows, ncols), order=1, preserve_range=True,
mode='constant')
# resize the image using nearest neighbour interpolation (order 0)
cloud_mask = transform.resize(cloud_mask, (nrows, ncols), order=0, preserve_range=True,
mode='constant').astype('bool_')
# check if -inf or nan values on any band and eventually add those pixels to cloud mask
for k in range(im_ms.shape[2]+1):
if k == 5:
im_inf = np.isin(im_pan, -np.inf)
im_nan = np.isnan(im_pan)
else:
im_inf = np.isin(im_ms[:,:,k], -np.inf)
im_nan = np.isnan(im_ms[:,:,k])
cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
# calculate cloud cover
cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
# pansharpen Green, Red, NIR (where there is overlapping with pan band in L7)
try:
im_ms_ps = pansharpen(im_ms[:,:,[1,2,3]], im_pan, cloud_mask)
except: # if pansharpening fails, keep downsampled bands (for long runs)
im_ms_ps = im_ms[:,:,[1,2,3]]
# add downsampled Blue and SWIR1 bands
im_ms_ps = np.append(im_ms[:,:,[0]], im_ms_ps, axis=2)
im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[4]], axis=2)
im_ms = im_ms_ps.copy()
#=============================================================================================#
# L8 images
#=============================================================================================#
elif satname == 'L8':
# read pan image
fn_pan = fn[0]
data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
georef = np.array(data.GetGeoTransform())
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
im_pan = np.stack(bands, 2)[:,:,0]
# size of pan image
nrows = im_pan.shape[0]
ncols = im_pan.shape[1]
# read ms image
fn_ms = fn[1]
data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
im_ms = np.stack(bands, 2)
# create cloud mask
im_qa = im_ms[:,:,5]
cloud_mask = create_cloud_mask(im_qa, satname)
# resize the image using bilinear interpolation (order 1)
im_ms = im_ms[:,:,:5]
im_ms = transform.resize(im_ms,(nrows, ncols), order=1, preserve_range=True,
mode='constant')
# resize the image using nearest neighbour interpolation (order 0)
cloud_mask = transform.resize(cloud_mask, (nrows, ncols), order=0, preserve_range=True,
mode='constant').astype('bool_')
# check if -inf or nan values on any band and eventually add those pixels to cloud mask
for k in range(im_ms.shape[2]+1):
if k == 5:
im_inf = np.isin(im_pan, -np.inf)
im_nan = np.isnan(im_pan)
else:
im_inf = np.isin(im_ms[:,:,k], -np.inf)
im_nan = np.isnan(im_ms[:,:,k])
cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
# calculate cloud cover
cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
# pansharpen Blue, Green, Red (where there is overlapping with pan band in L8)
try:
im_ms_ps = pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask)
except: # if pansharpening fails, keep downsampled bands (for long runs)
im_ms_ps = im_ms[:,:,[0,1,2]]
# add downsampled NIR and SWIR1 bands
im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2)
im_ms = im_ms_ps.copy()
#=============================================================================================#
# S2 images
#=============================================================================================#
if satname == 'S2':
# read 10m bands (R,G,B,NIR)
fn10 = fn[0]
data = gdal.Open(fn10, gdal.GA_ReadOnly)
georef = np.array(data.GetGeoTransform())
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
im10 = np.stack(bands, 2)
im10 = im10/10000 # TOA scaled to 10000
# if image contains only zeros (can happen with S2), skip the image
if sum(sum(sum(im10))) < 1:
im_ms = []
georef = []
# skip the image by giving it a full cloud_mask
cloud_mask = np.ones((im10.shape[0],im10.shape[1])).astype('bool')
return im_ms, georef, cloud_mask
# size of 10m bands
nrows = im10.shape[0]
ncols = im10.shape[1]
# read 20m band (SWIR1)
fn20 = fn[1]
data = gdal.Open(fn20, gdal.GA_ReadOnly)
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
im20 = np.stack(bands, 2)
im20 = im20[:,:,0]
im20 = im20/10000 # TOA scaled to 10000
# resize the image using bilinear interpolation (order 1)
im_swir = transform.resize(im20, (nrows, ncols), order=1, preserve_range=True,
mode='constant')
im_swir = np.expand_dims(im_swir, axis=2)
# append down-sampled SWIR1 band to the other 10m bands
im_ms = np.append(im10, im_swir, axis=2)
# create cloud mask using 60m QA band (not as good as Landsat cloud cover)
fn60 = fn[2]
data = gdal.Open(fn60, gdal.GA_ReadOnly)
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
im60 = np.stack(bands, 2)
im_qa = im60[:,:,0]
cloud_mask = create_cloud_mask(im_qa, satname)
# resize the cloud mask using nearest neighbour interpolation (order 0)
cloud_mask = transform.resize(cloud_mask,(nrows, ncols), order=0, preserve_range=True,
mode='constant')
# check if -inf or nan values on any band and add to cloud mask
for k in range(im_ms.shape[2]):
im_inf = np.isin(im_ms[:,:,k], -np.inf)
im_nan = np.isnan(im_ms[:,:,k])
cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
# calculate cloud cover
cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
return im_ms, georef, cloud_mask
def create_jpg(im_ms, cloud_mask, date, satname, filepath):
"""
Saves a .jpg file with the RGB image as well as the NIR and SWIR1 grayscale images.
KV WRL 2018
Arguments:
-----------
im_ms: np.array
3D array containing the pansharpened/down-sampled bands (B,G,R,NIR,SWIR1)
cloud_mask: np.array
2D cloud mask with True where cloud pixels are
date: str
String containing the date at which the image was acquired
satname: str
name of the satellite mission (e.g., 'L5')
Returns:
-----------
Saves a .jpg image corresponding to the preprocessed satellite image
"""
# rescale image intensity for display purposes
im_RGB = rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9)
im_NIR = rescale_image_intensity(im_ms[:,:,3], cloud_mask, 99.9)
im_SWIR = rescale_image_intensity(im_ms[:,:,4], cloud_mask, 99.9)
# make figure
fig = plt.figure()
fig.set_size_inches([18,9])
fig.set_tight_layout(True)
# RGB
plt.subplot(131)
plt.axis('off')
plt.imshow(im_RGB)
plt.title(date + ' ' + satname, fontsize=16)
# NIR
plt.subplot(132)
plt.axis('off')
plt.imshow(im_NIR, cmap='seismic')
plt.title('Near Infrared', fontsize=16)
# SWIR
plt.subplot(133)
plt.axis('off')
plt.imshow(im_SWIR, cmap='seismic')
plt.title('Short-wave Infrared', fontsize=16)
# save figure
plt.rcParams['savefig.jpeg_quality'] = 100
fig.savefig(os.path.join(filepath,
date + '_' + satname + '.jpg'), dpi=150)
plt.close()
def preprocess_all_images(metadata, settings):
"""
Saves a .jpg image for all the file contained in metadata.
KV WRL 2018
Arguments:
-----------
sitename: str
name of the site (and corresponding folder)
metadata: dict
contains all the information about the satellite images that were downloaded
cloud_thresh: float
maximum fraction of cloud cover allowed in the images
Returns:
-----------
Generates .jpg files for all the satellite images avaialble
"""
sitename = settings['sitename']
cloud_thresh = settings['cloud_thresh']
# create subfolder to store the jpg files
filepath_jpg = os.path.join(os.getcwd(), 'data', sitename, 'jpg_files', 'preprocessed')
try:
os.makedirs(filepath_jpg)
except:
print('')
# loop through satellite list
for satname in metadata.keys():
# access the images
if satname == 'L5':
# access downloaded Landsat 5 images
filepath = os.path.join(os.getcwd(), 'data', sitename, satname, '30m')
filenames = os.listdir(filepath)
elif satname == 'L7':
# access downloaded Landsat 7 images
filepath_pan = os.path.join(os.getcwd(), 'data', sitename, 'L7', 'pan')
filepath_ms = os.path.join(os.getcwd(), 'data', sitename, 'L7', 'ms')
filenames_pan = os.listdir(filepath_pan)
filenames_ms = os.listdir(filepath_ms)
if (not len(filenames_pan) == len(filenames_ms)):
raise 'error: not the same amount of files for pan and ms'
filepath = [filepath_pan, filepath_ms]
filenames = filenames_pan
elif satname == 'L8':
# access downloaded Landsat 7 images
filepath_pan = os.path.join(os.getcwd(), 'data', sitename, 'L8', 'pan')
filepath_ms = os.path.join(os.getcwd(), 'data', sitename, 'L8', 'ms')
filenames_pan = os.listdir(filepath_pan)
filenames_ms = os.listdir(filepath_ms)
if (not len(filenames_pan) == len(filenames_ms)):
raise 'error: not the same amount of files for pan and ms'
filepath = [filepath_pan, filepath_ms]
filenames = filenames_pan
elif satname == 'S2':
# access downloaded Sentinel 2 images
filepath10 = os.path.join(os.getcwd(), 'data', sitename, satname, '10m')
filenames10 = os.listdir(filepath10)
filepath20 = os.path.join(os.getcwd(), 'data', sitename, satname, '20m')
filenames20 = os.listdir(filepath20)
filepath60 = os.path.join(os.getcwd(), 'data', sitename, satname, '60m')
filenames60 = os.listdir(filepath60)
if (not len(filenames10) == len(filenames20)) or (not len(filenames20) == len(filenames60)):
raise 'error: not the same amount of files for 10, 20 and 60 m'
filepath = [filepath10, filepath20, filepath60]
filenames = filenames10
# loop through images
for i in range(len(filenames)):
# image filename
fn = SDS_tools.get_filenames(filenames[i],filepath, satname)
# preprocess image (cloud mask + pansharpening/downsampling)
im_ms, georef, cloud_mask = preprocess_single(fn, satname)
# calculate cloud cover
cloud_cover = np.divide(sum(sum(cloud_mask.astype(int))),
(cloud_mask.shape[0]*cloud_mask.shape[1]))
# skip image if cloud cover is above threshold
if cloud_cover > cloud_thresh:
continue
# save .jpg with date and satellite in the title
date = filenames[i][:10]
create_jpg(im_ms, cloud_mask, date, satname, filepath_jpg)
def get_reference_sl(metadata, settings):
sitename = settings['sitename']
# check if reference shoreline already exists
filepath = os.path.join(os.getcwd(), 'data', sitename)
filename = sitename + '_ref_sl.pkl'
if filename in os.listdir(filepath):
print('Reference shoreline already exists and was loaded')
with open(os.path.join(filepath, sitename + '_ref_sl.pkl'), 'rb') as f:
refsl = pickle.load(f)
return refsl
else:
satname = 'S2'
# access downloaded Sentinel 2 images
filepath10 = os.path.join(os.getcwd(), 'data', sitename, satname, '10m')
filenames10 = os.listdir(filepath10)
filepath20 = os.path.join(os.getcwd(), 'data', sitename, satname, '20m')
filenames20 = os.listdir(filepath20)
filepath60 = os.path.join(os.getcwd(), 'data', sitename, satname, '60m')
filenames60 = os.listdir(filepath60)
if (not len(filenames10) == len(filenames20)) or (not len(filenames20) == len(filenames60)):
raise 'error: not the same amount of files for 10, 20 and 60 m'
for i in range(len(filenames10)):
# image filename
fn = [os.path.join(filepath10, filenames10[i]),
os.path.join(filepath20, filenames20[i]),
os.path.join(filepath60, filenames60[i])]
# preprocess image (cloud mask + pansharpening/downsampling)
im_ms, georef, cloud_mask = preprocess_single(fn, satname)
# calculate cloud cover
cloud_cover = np.divide(sum(sum(cloud_mask.astype(int))),
(cloud_mask.shape[0]*cloud_mask.shape[1]))
# skip image if cloud cover is above threshold
if cloud_cover > settings['cloud_thresh']:
continue
# rescale image intensity for display purposes
im_RGB = rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9)
# make figure
fig = plt.figure()
fig.set_size_inches([18,9])
fig.set_tight_layout(True)
# RGB
plt.axis('off')
plt.imshow(im_RGB)
plt.title('click <skip> if image is not clear enough to digitize the shoreline.\n' +
'Otherwise click on <keep> and start digitizing the shoreline.\n' +
'When finished digitizing the shoreline click on the scroll wheel ' +
'(middle click).', fontsize=14)
plt.text(0, 0.9, 'keep', size=16, ha="left", va="top",
transform=plt.gca().transAxes,
bbox=dict(boxstyle="square", ec='k',fc='w'))
plt.text(1, 0.9, 'skip', size=16, ha="right", va="top",
transform=plt.gca().transAxes,
bbox=dict(boxstyle="square", ec='k',fc='w'))
mng = plt.get_current_fig_manager()
mng.window.showMaximized()
# let user click on the image once
pt_keep = ginput(n=1, timeout=100, show_clicks=True)
pt_keep = np.array(pt_keep)
# if clicks next to <skip>, show another image
if pt_keep[0][0] > im_ms.shape[1]/2:
plt.close()
continue
else:
# let user click on the shoreline
pts = ginput(n=5000, timeout=100000, show_clicks=True)
pts_pix = np.array(pts)
plt.close()
# convert image coordinates to world coordinates
pts_world = SDS_tools.convert_pix2world(pts_pix[:,[1,0]], georef)
image_epsg = metadata[satname]['epsg'][i]
pts_coords = SDS_tools.convert_epsg(pts_world, image_epsg, settings['output_epsg'])
with open(os.path.join(filepath, sitename + '_ref_sl.pkl'), 'wb') as f:
pickle.dump(pts_coords, f)
print('Reference shoreline has been saved')
break
return pts_coords

604
SDS_shoreline.py
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"""This module contains all the functions needed for extracting satellite-derived shorelines (SDS)
Author: Kilian Vos, Water Research Laboratory, University of New South Wales
"""
# Initial settings
import os
import numpy as np
import matplotlib.pyplot as plt
import pdb
# other modules
from osgeo import gdal, ogr, osr
import scipy.interpolate as interpolate
from datetime import datetime, timedelta
import matplotlib.patches as mpatches
import matplotlib.lines as mlines
import matplotlib.cm as cm
from matplotlib import gridspec
from pylab import ginput
import pickle
# image processing modules
import skimage.filters as filters
import skimage.exposure as exposure
import skimage.transform as transform
import sklearn.decomposition as decomposition
import skimage.measure as measure
import skimage.morphology as morphology
# machine learning modules
from sklearn.externals import joblib
from shapely.geometry import LineString
import SDS_tools, SDS_preprocess
np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
def nd_index(im1, im2, cloud_mask):
"""
Computes normalised difference index on 2 images (2D), given a cloud mask (2D).
KV WRL 2018
Arguments:
-----------
im1, im2: np.array
Images (2D) with which to calculate the ND index
cloud_mask: np.array
2D cloud mask with True where cloud pixels are
Returns: -----------
im_nd: np.array
Image (2D) containing the ND index
"""
# reshape the cloud mask
vec_mask = cloud_mask.reshape(im1.shape[0] * im1.shape[1])
# initialise with NaNs
vec_nd = np.ones(len(vec_mask)) * np.nan
# reshape the two images
vec1 = im1.reshape(im1.shape[0] * im1.shape[1])
vec2 = im2.reshape(im2.shape[0] * im2.shape[1])
# compute the normalised difference index
temp = np.divide(vec1[~vec_mask] - vec2[~vec_mask],
vec1[~vec_mask] + vec2[~vec_mask])
vec_nd[~vec_mask] = temp
# reshape into image
im_nd = vec_nd.reshape(im1.shape[0], im1.shape[1])
return im_nd
def classify_image_NN(im_ms_ps, im_pan, cloud_mask, min_beach_size):
"""
Classifies every pixel in the image in one of 4 classes:
- sand --> label = 1
- whitewater (breaking waves and swash) --> label = 2
- water --> label = 3
- other (vegetation, buildings, rocks...) --> label = 0
The classifier is a Neural Network, trained with 7000 pixels for the class SAND and 1500
pixels for each of the other classes. This is because the class of interest for my application
is SAND and I wanted to minimize the classification error for that class.
KV WRL 2018
Arguments:
-----------
im_ms_ps: np.array
Pansharpened RGB + downsampled NIR and SWIR
im_pan:
Panchromatic band
cloud_mask: np.array
2D cloud mask with True where cloud pixels are
plot_bool: boolean
True if plot is wanted
Returns: -----------
im_classif: np.array
2D image containing labels
im_labels: np.array of booleans
3D image containing a boolean image for each class (im_classif == label)
"""
# load classifier
clf = joblib.load('.\\classifiers\\NN_4classes_withpan.pkl')
# calculate features
n_features = 10
im_features = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1], n_features))
im_features[:,:,[0,1,2,3,4]] = im_ms_ps
im_features[:,:,5] = im_pan
im_features[:,:,6] = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, False) # (NIR-G)
im_features[:,:,7] = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,2], cloud_mask, False) # ND(NIR-R)
im_features[:,:,8] = nd_index(im_ms_ps[:,:,0], im_ms_ps[:,:,2], cloud_mask, False) # ND(B-R)
im_features[:,:,9] = nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, False) # ND(SWIR-G)
# remove NaNs and clouds
vec_features = im_features.reshape((im_ms_ps.shape[0] * im_ms_ps.shape[1], n_features))
vec_cloud = cloud_mask.reshape(cloud_mask.shape[0]*cloud_mask.shape[1])
vec_nan = np.any(np.isnan(vec_features), axis=1)
vec_mask = np.logical_or(vec_cloud, vec_nan)
vec_features = vec_features[~vec_mask, :]
# predict with NN classifier
labels = clf.predict(vec_features)
# recompose image
vec_classif = np.zeros((cloud_mask.shape[0]*cloud_mask.shape[1]))
vec_classif[~vec_mask] = labels
im_classif = vec_classif.reshape((im_ms_ps.shape[0], im_ms_ps.shape[1]))
# labels
im_sand = im_classif == 1
# remove small patches of sand
im_sand = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2)
im_swash = im_classif == 2
im_water = im_classif == 3
im_labels = np.stack((im_sand,im_swash,im_water), axis=-1)
return im_classif, im_labels
def classify_image_NN_nopan(im_ms_ps, cloud_mask, min_beach_size):
"""
To be used for multispectral images that do not have a panchromatic band (L5 and S2).
Classifies every pixel in the image in one of 4 classes:
- sand --> label = 1
- whitewater (breaking waves and swash) --> label = 2
- water --> label = 3
- other (vegetation, buildings, rocks...) --> label = 0
The classifier is a Neural Network, trained with 7000 pixels for the class SAND and 1500
pixels for each of the other classes. This is because the class of interest for my application
is SAND and I wanted to minimize the classification error for that class.
KV WRL 2018
Arguments:
-----------
im_ms_ps: np.array
Pansharpened RGB + downsampled NIR and SWIR
im_pan:
Panchromatic band
cloud_mask: np.array
2D cloud mask with True where cloud pixels are
Returns: -----------
im_classif: np.ndarray
2D image containing labels
im_labels: np.ndarray of booleans
3D image containing a boolean image for each class (im_classif == label)
"""
# load classifier
clf = joblib.load('.\\classifiers\\NN_4classes_nopan.pkl')
# calculate features
n_features = 9
im_features = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1], n_features))
im_features[:,:,[0,1,2,3,4]] = im_ms_ps
im_features[:,:,5] = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask) # (NIR-G)
im_features[:,:,6] = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,2], cloud_mask) # ND(NIR-R)
im_features[:,:,7] = nd_index(im_ms_ps[:,:,0], im_ms_ps[:,:,2], cloud_mask) # ND(B-R)
im_features[:,:,8] = nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask) # ND(SWIR-G)
# remove NaNs and clouds
vec_features = im_features.reshape((im_ms_ps.shape[0] * im_ms_ps.shape[1], n_features))
vec_cloud = cloud_mask.reshape(cloud_mask.shape[0]*cloud_mask.shape[1])
vec_nan = np.any(np.isnan(vec_features), axis=1)
vec_mask = np.logical_or(vec_cloud, vec_nan)
vec_features = vec_features[~vec_mask, :]
# predict with NN classifier
labels = clf.predict(vec_features)
# recompose image
vec_classif = np.zeros((cloud_mask.shape[0]*cloud_mask.shape[1]))
vec_classif[~vec_mask] = labels
im_classif = vec_classif.reshape((im_ms_ps.shape[0], im_ms_ps.shape[1]))
# labels
im_sand = im_classif == 1
# remove small patches of sand
im_sand = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2)
im_swash = im_classif == 2
im_water = im_classif == 3
im_labels = np.stack((im_sand,im_swash,im_water), axis=-1)
return im_classif, im_labels
def find_wl_contours1(im_ndwi, cloud_mask):
"""
Traditional method for shorelien detection.
Finds the water line by thresholding the Normalized Difference Water Index and applying
the Marching Squares Algorithm to contour the iso-value corresponding to the threshold.
KV WRL 2018
Arguments:
-----------
im_ndwi: np.ndarray
Image (2D) with the NDWI (water index)
cloud_mask: np.ndarray
2D cloud mask with True where cloud pixels are
Returns: -----------
contours_wl: list of np.arrays
contains the (row,column) coordinates of the contour lines
"""
# reshape image to vector
vec_ndwi = im_ndwi.reshape(im_ndwi.shape[0] * im_ndwi.shape[1])
vec_mask = cloud_mask.reshape(cloud_mask.shape[0] * cloud_mask.shape[1])
vec = vec_ndwi[~vec_mask]
# apply otsu's threshold
vec = vec[~np.isnan(vec)]
t_otsu = filters.threshold_otsu(vec)
# use Marching Squares algorithm to detect contours on ndwi image
contours = measure.find_contours(im_ndwi, t_otsu)
# remove contours that have nans (due to cloud pixels in the contour)
contours_nonans = []
for k in range(len(contours)):
if np.any(np.isnan(contours[k])):
index_nan = np.where(np.isnan(contours[k]))[0]
contours_temp = np.delete(contours[k], index_nan, axis=0)
if len(contours_temp) > 1:
contours_nonans.append(contours_temp)
else:
contours_nonans.append(contours[k])
contours = contours_nonans
return contours
def find_wl_contours2(im_ms_ps, im_labels, cloud_mask, buffer_size):
"""
New robust method for extracting shorelines. Incorporates the classification component to
refube the treshold and make it specific to the sand/water interface.
KV WRL 2018
Arguments:
-----------
im_ms_ps: np.array
Pansharpened RGB + downsampled NIR and SWIR
im_labels: np.array
3D image containing a boolean image for each class in the order (sand, swash, water)
cloud_mask: np.array
2D cloud mask with True where cloud pixels are
buffer_size: int
size of the buffer around the sandy beach
Returns: -----------
contours_wi: list of np.arrays
contains the (row,column) coordinates of the contour lines extracted with the
NDWI (Normalized Difference Water Index)
contours_mwi: list of np.arrays
contains the (row,column) coordinates of the contour lines extracted with the
MNDWI (Modified Normalized Difference Water Index)
"""
nrows = cloud_mask.shape[0]
ncols = cloud_mask.shape[1]
# calculate Normalized Difference Modified Water Index (SWIR - G)
im_mwi = nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask)
# calculate Normalized Difference Modified Water Index (NIR - G)
im_wi = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask)
# stack indices together
im_ind = np.stack((im_wi, im_mwi), axis=-1)
vec_ind = im_ind.reshape(nrows*ncols,2)
# reshape labels into vectors
vec_sand = im_labels[:,:,0].reshape(ncols*nrows)
vec_water = im_labels[:,:,2].reshape(ncols*nrows)
# create a buffer around the sandy beach
se = morphology.disk(buffer_size)
im_buffer = morphology.binary_dilation(im_labels[:,:,0], se)
vec_buffer = im_buffer.reshape(nrows*ncols)
# select water/sand/swash pixels that are within the buffer
int_water = vec_ind[np.logical_and(vec_buffer,vec_water),:]
int_sand = vec_ind[np.logical_and(vec_buffer,vec_sand),:]
# make sure both classes have the same number of pixels before thresholding
if len(int_water) > 0 and len(int_sand) > 0:
if np.argmin([int_sand.shape[0],int_water.shape[0]]) == 1:
if (int_sand.shape[0] - int_water.shape[0])/int_water.shape[0] > 0.5:
int_sand = int_sand[np.random.randint(0,int_sand.shape[0],int_water.shape[0]),:]
else:
if (int_water.shape[0] - int_sand.shape[0])/int_sand.shape[0] > 0.5:
int_water = int_water[np.random.randint(0,int_water.shape[0],int_sand.shape[0]),:]
# threshold the sand/water intensities
int_all = np.append(int_water,int_sand, axis=0)
t_mwi = filters.threshold_otsu(int_all[:,0])
t_wi = filters.threshold_otsu(int_all[:,1])
# find contour with MS algorithm
im_wi_buffer = np.copy(im_wi)
im_wi_buffer[~im_buffer] = np.nan
im_mwi_buffer = np.copy(im_mwi)
im_mwi_buffer[~im_buffer] = np.nan
contours_wi = measure.find_contours(im_wi_buffer, t_wi)
contours_mwi = measure.find_contours(im_mwi, t_mwi)
# remove contour points that are nans (around clouds)
contours = contours_wi
contours_nonans = []
for k in range(len(contours)):
if np.any(np.isnan(contours[k])):
index_nan = np.where(np.isnan(contours[k]))[0]
contours_temp = np.delete(contours[k], index_nan, axis=0)
if len(contours_temp) > 1:
contours_nonans.append(contours_temp)
else:
contours_nonans.append(contours[k])
contours_wi = contours_nonans
contours = contours_mwi
contours_nonans = []
for k in range(len(contours)):
if np.any(np.isnan(contours[k])):
index_nan = np.where(np.isnan(contours[k]))[0]
contours_temp = np.delete(contours[k], index_nan, axis=0)
if len(contours_temp) > 1:
contours_nonans.append(contours_temp)
else:
contours_nonans.append(contours[k])
contours_mwi = contours_nonans
return contours_wi, contours_mwi
def process_shoreline(contours, georef, image_epsg, settings):
# convert pixel coordinates to world coordinates
contours_world = SDS_tools.convert_pix2world(contours, georef)
# convert world coordinates to desired spatial reference system
contours_epsg = SDS_tools.convert_epsg(contours_world, image_epsg, settings['output_epsg'])
# remove contours that have a perimeter < min_length_wl (provided in settings dict)
# this enable to remove the very small contours that do not correspond to the shoreline
contours_long = []
for l, wl in enumerate(contours_epsg):
coords = [(wl[k,0], wl[k,1]) for k in range(len(wl))]
a = LineString(coords) # shapely LineString structure
if a.length >= settings['min_length_sl']:
contours_long.append(wl)
# format points into np.array
x_points = np.array([])
y_points = np.array([])
for k in range(len(contours_long)):
x_points = np.append(x_points,contours_long[k][:,0])
y_points = np.append(y_points,contours_long[k][:,1])
contours_array = np.transpose(np.array([x_points,y_points]))
# if reference shoreline has been manually digitised
if 'refsl' in settings.keys():
# only keep the points that are at a certain distance (define in settings) from the
# reference shoreline, enables to avoid false detections and remove obvious outliers
temp = np.zeros((len(contours_array))).astype(bool)
for k in range(len(settings['refsl'])):
temp = np.logical_or(np.linalg.norm(contours_array - settings['refsl'][k,[0,1]],
axis=1) < settings['max_dist_ref'], temp)
shoreline = contours_array[temp]
else:
shoreline = contours_array
return shoreline
def show_detection(im_ms, cloud_mask, im_labels, shoreline,image_epsg, georef,
settings, date, satname):
# subfolder to store the .jpg files
filepath = os.path.join(os.getcwd(), 'data', settings['sitename'], 'jpg_files', 'detection')
# display RGB image
im_RGB = SDS_preprocess.rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9)
# display classified image
im_class = np.copy(im_RGB)
cmap = cm.get_cmap('tab20c')
colorpalette = cmap(np.arange(0,13,1))
colours = np.zeros((3,4))
colours[0,:] = colorpalette[5]
colours[1,:] = np.array([204/255,1,1,1])
colours[2,:] = np.array([0,91/255,1,1])
for k in range(0,im_labels.shape[2]):
im_class[im_labels[:,:,k],0] = colours[k,0]
im_class[im_labels[:,:,k],1] = colours[k,1]
im_class[im_labels[:,:,k],2] = colours[k,2]
# display MNDWI grayscale image
im_mwi = nd_index(im_ms[:,:,4], im_ms[:,:,1], cloud_mask)
# transform world coordinates of shoreline into pixel coordinates
sl_pix = SDS_tools.convert_world2pix(SDS_tools.convert_epsg(shoreline, settings['output_epsg'],
image_epsg)[:,[0,1]], georef)
# make figure
fig = plt.figure()
gs = gridspec.GridSpec(1, 3)
gs.update(bottom=0.05, top=0.95)
ax1 = fig.add_subplot(gs[0,0])
plt.imshow(im_RGB)
plt.plot(sl_pix[:,0], sl_pix[:,1], 'k--')
plt.axis('off')
ax1.set_anchor('W')
btn_keep = plt.text(0, 0.9, 'keep', size=16, ha="left", va="top",
transform=ax1.transAxes,
bbox=dict(boxstyle="square", ec='k',fc='w'))
btn_skip = plt.text(1, 0.9, 'skip', size=16, ha="right", va="top",
transform=ax1.transAxes,
bbox=dict(boxstyle="square", ec='k',fc='w'))
plt.title('Click on <keep> if shoreline detection is correct. Click on <skip> if false detection')
ax2 = fig.add_subplot(gs[0,1])
plt.imshow(im_class)
plt.plot(sl_pix[:,0], sl_pix[:,1], 'k--')
plt.axis('off')
ax2.set_anchor('W')
orange_patch = mpatches.Patch(color=colours[0,:], label='sand')
white_patch = mpatches.Patch(color=colours[1,:], label='whitewater')
blue_patch = mpatches.Patch(color=colours[2,:], label='water')
black_line = mlines.Line2D([],[],color='k',linestyle='--', label='shoreline')
plt.legend(handles=[orange_patch,white_patch,blue_patch, black_line], bbox_to_anchor=(1, 0.5), fontsize=9)
ax3 = fig.add_subplot(gs[0,2])
plt.imshow(im_mwi, cmap='bwr')
plt.plot(sl_pix[:,0], sl_pix[:,1], 'k--')
plt.axis('off')
cb = plt.colorbar()
cb.ax.tick_params(labelsize=10)
cb.set_label('MNDWI values')
ax3.set_anchor('W')
fig.set_size_inches([12.53, 9.3])
fig.set_tight_layout(True)
mng = plt.get_current_fig_manager()
mng.window.showMaximized()
# wait for user's selection (<keep> or <skip>)
pt = ginput(n=1, timeout=100, show_clicks=True)
pt = np.array(pt)
# if clicks next to <skip>, return skip_image = True
if pt[0][0] > im_ms.shape[1]/2:
skip_image = True
plt.close()
else:
skip_image = False
ax1.set_title(date + ' ' + satname)
btn_skip.set_visible(False)
btn_keep.set_visible(False)
fig.savefig(os.path.join(filepath, date + '_' + satname + '.jpg'), dpi=150)
plt.close()
return skip_image
def extract_shorelines(metadata, settings):
sitename = settings['sitename']
# initialise output structure
out = dict([])
# create a subfolder to store the .jpg images showing the detection
filepath_jpg = os.path.join(os.getcwd(), 'data', sitename, 'jpg_files', 'detection')
try:
os.makedirs(filepath_jpg)
except:
print('')
# loop through satellite list
for satname in metadata.keys():
# access the images
if satname == 'L5':
# access downloaded Landsat 5 images
filepath = os.path.join(os.getcwd(), 'data', sitename, satname, '30m')
filenames = os.listdir(filepath)
elif satname == 'L7':
# access downloaded Landsat 7 images
filepath_pan = os.path.join(os.getcwd(), 'data', sitename, 'L7', 'pan')
filepath_ms = os.path.join(os.getcwd(), 'data', sitename, 'L7', 'ms')
filenames_pan = os.listdir(filepath_pan)
filenames_ms = os.listdir(filepath_ms)
if (not len(filenames_pan) == len(filenames_ms)):
raise 'error: not the same amount of files for pan and ms'
filepath = [filepath_pan, filepath_ms]
filenames = filenames_pan
elif satname == 'L8':
# access downloaded Landsat 7 images
filepath_pan = os.path.join(os.getcwd(), 'data', sitename, 'L8', 'pan')
filepath_ms = os.path.join(os.getcwd(), 'data', sitename, 'L8', 'ms')
filenames_pan = os.listdir(filepath_pan)
filenames_ms = os.listdir(filepath_ms)
if (not len(filenames_pan) == len(filenames_ms)):
raise 'error: not the same amount of files for pan and ms'
filepath = [filepath_pan, filepath_ms]
filenames = filenames_pan
elif satname == 'S2':
# access downloaded Sentinel 2 images
filepath10 = os.path.join(os.getcwd(), 'data', sitename, satname, '10m')
filenames10 = os.listdir(filepath10)
filepath20 = os.path.join(os.getcwd(), 'data', sitename, satname, '20m')
filenames20 = os.listdir(filepath20)
filepath60 = os.path.join(os.getcwd(), 'data', sitename, satname, '60m')
filenames60 = os.listdir(filepath60)
if (not len(filenames10) == len(filenames20)) or (not len(filenames20) == len(filenames60)):
raise 'error: not the same amount of files for 10, 20 and 60 m'
filepath = [filepath10, filepath20, filepath60]
filenames = filenames10
# initialise some variables
out_timestamp = [] # datetime at which the image was acquired (UTC time)
out_shoreline = [] # vector of shoreline points
out_filename = [] # filename of the images from which the shorelines where derived
out_cloudcover = [] # cloud cover of the images
out_geoaccuracy = []# georeferencing accuracy of the images
out_idxkeep = [] # index that were kept during the analysis (cloudy images are skipped)
# loop through the images
for i in range(len(filenames)):
# get image filename
fn = SDS_tools.get_filenames(filenames[i],filepath, satname)
# preprocess image (cloud mask + pansharpening/downsampling)
im_ms, georef, cloud_mask = SDS_preprocess.preprocess_single(fn, satname)
# get image spatial reference system (epsg code) from metadata dict
image_epsg = metadata[satname]['epsg'][i]
# calculate cloud cover
cloud_cover = np.divide(sum(sum(cloud_mask.astype(int))),
(cloud_mask.shape[0]*cloud_mask.shape[1]))
# skip image if cloud cover is above threshold
if cloud_cover > settings['cloud_thresh']:
continue
# classify image in 4 classes (sand, whitewater, water, other) with NN classifier
im_classif, im_labels = classify_image_NN_nopan(im_ms, cloud_mask,
settings['min_beach_size'])
# extract water line contours
# if there aren't any sandy pixels, use find_wl_contours1 (traditional method),
# otherwise use find_wl_contours2 (enhanced method with classification)
if sum(sum(im_labels[:,:,0])) == 0 :
# compute MNDWI (SWIR-Green normalized index) grayscale image
im_mndwi = nd_index(im_ms[:,:,4], im_ms[:,:,1], cloud_mask)
# find water contourson MNDWI grayscale image
contours_mwi = find_wl_contours1(im_mndwi, cloud_mask)
else:
# use classification to refine threshold and extract sand/water interface
contours_wi, contours_mwi = find_wl_contours2(im_ms, im_labels,
cloud_mask, settings['buffer_size'])
# extract clean shoreline from water contours
shoreline = process_shoreline(contours_mwi, georef, image_epsg, settings)
if settings['check_detection']:
date = filenames[i][:10]
skip_image = show_detection(im_ms, cloud_mask, im_labels, shoreline,
image_epsg, georef, settings, date, satname)
if skip_image:
continue
# fill and save output structure
out_timestamp.append(metadata[satname]['dates'][i])
out_shoreline.append(shoreline)
out_filename.append(filenames[i])
out_cloudcover.append(cloud_cover)
out_geoaccuracy.append(metadata[satname]['acc_georef'][i])
out_idxkeep.append(i)
out[satname] = {
'timestamp': out_timestamp,
'shoreline': out_shoreline,
'filename': out_filename,
'cloudcover': out_cloudcover,
'geoaccuracy': out_geoaccuracy,
'idxkeep': out_idxkeep
}
# add some metadata
out['meta'] = {
'timestamp': 'UTC time',
'shoreline': 'coordinate system epsg : ' + str(settings['output_epsg']),
'cloudcover': 'calculated on the cropped image',
'geoaccuracy': 'RMSE error based on GCPs',
'idxkeep': 'indices of the images that were kept to extract a shoreline'
}
# save output structure as out.pkl
filepath = os.path.join(os.getcwd(), 'data', sitename)
with open(os.path.join(filepath, sitename + '_out.pkl'), 'wb') as f:
pickle.dump(out, f)
return out

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SDS_tools.py
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"""This module contains utilities to work with satellite images'
Author: Kilian Vos, Water Research Laboratory, University of New South Wales
"""
# Initial settings
import os
import numpy as np
from osgeo import gdal, ogr, osr
import skimage.transform as transform
import simplekml
import pdb
# Functions
def convert_pix2world(points, georef):
"""
Converts pixel coordinates (row,columns) to world projected coordinates
performing an affine transformation.
KV WRL 2018
Arguments:
-----------
points: np.array or list of np.array
array with 2 columns (rows first and columns second)
georef: np.array
vector of 6 elements [Xtr, Xscale, Xshear, Ytr, Yshear, Yscale]
Returns: -----------
points_converted: np.array or list of np.array
converted coordinates, first columns with X and second column with Y
"""
# make affine transformation matrix
aff_mat = np.array([[georef[1], georef[2], georef[0]],
[georef[4], georef[5], georef[3]],
[0, 0, 1]])
# create affine transformation
tform = transform.AffineTransform(aff_mat)
if type(points) is list:
points_converted = []
# iterate over the list
for i, arr in enumerate(points):
tmp = arr[:,[1,0]]
points_converted.append(tform(tmp))
elif type(points) is np.ndarray:
tmp = points[:,[1,0]]
points_converted = tform(tmp)
else:
print('invalid input type')
raise
return points_converted
def convert_world2pix(points, georef):
"""
Converts world projected coordinates (X,Y) to image coordinates (row,column)
performing an affine transformation.
KV WRL 2018
Arguments:
-----------
points: np.array or list of np.array
array with 2 columns (rows first and columns second)
georef: np.array
vector of 6 elements [Xtr, Xscale, Xshear, Ytr, Yshear, Yscale]
Returns: -----------
points_converted: np.array or list of np.array
converted coordinates, first columns with row and second column with column
"""
# make affine transformation matrix
aff_mat = np.array([[georef[1], georef[2], georef[0]],
[georef[4], georef[5], georef[3]],
[0, 0, 1]])
# create affine transformation
tform = transform.AffineTransform(aff_mat)
if type(points) is list:
points_converted = []
# iterate over the list
for i, arr in enumerate(points):
points_converted.append(tform.inverse(points))
elif type(points) is np.ndarray:
points_converted = tform.inverse(points)
else:
print('invalid input type')
raise
return points_converted
def convert_epsg(points, epsg_in, epsg_out):
"""
Converts from one spatial reference to another using the epsg codes.
KV WRL 2018
Arguments:
-----------
points: np.array or list of np.ndarray
array with 2 columns (rows first and columns second)
epsg_in: int
epsg code of the spatial reference in which the input is
epsg_out: int
epsg code of the spatial reference in which the output will be
Returns: -----------
points_converted: np.array or list of np.array
converted coordinates
"""
# define input and output spatial references
inSpatialRef = osr.SpatialReference()
inSpatialRef.ImportFromEPSG(epsg_in)
outSpatialRef = osr.SpatialReference()
outSpatialRef.ImportFromEPSG(epsg_out)
# create a coordinates transform
coordTransform = osr.CoordinateTransformation(inSpatialRef, outSpatialRef)
# transform points
if type(points) is list:
points_converted = []
# iterate over the list
for i, arr in enumerate(points):
points_converted.append(np.array(coordTransform.TransformPoints(arr)))
elif type(points) is np.ndarray:
points_converted = np.array(coordTransform.TransformPoints(points))
else:
print('invalid input type')
raise
return points_converted
def coords_from_kml(fn):
# read .kml file
with open(fn) as kmlFile:
doc = kmlFile.read()
# parse to find coordinates field
str1 = '<coordinates>'
str2 = '</coordinates>'
subdoc = doc[doc.find(str1)+len(str1):doc.find(str2)]
coordlist = subdoc.split('\n')
polygon = []
for i in range(1,len(coordlist)-1):
polygon.append([float(coordlist[i].split(',')[0]), float(coordlist[i].split(',')[1])])
return [polygon]
def save_kml(coords, epsg):
kml = simplekml.Kml()
coords_wgs84 = convert_epsg(coords, epsg, 4326)
kml.newlinestring(name='coords', coords=coords_wgs84)
kml.save('coords.kml')
def get_filenames(filename, filepath, satname):
if satname == 'L5':
fn = os.path.join(filepath, filename)
if satname == 'L7' or satname == 'L8':
idx = filename.find('.tif')
filename_ms = filename[:idx-3] + 'ms.tif'
fn = [os.path.join(filepath[0], filename),
os.path.join(filepath[1], filename_ms)]
if satname == 'S2':
idx = filename.find('.tif')
filename20 = filename[:idx-3] + '20m.tif'
filename60 = filename[:idx-3] + '60m.tif'
fn = [os.path.join(filepath[0], filename),
os.path.join(filepath[1], filename20),
os.path.join(filepath[2], filename60)]
return fn

80
main_spyder.py
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#==========================================================#
# Shoreline extraction from satellite images
#==========================================================#
# load modules
import os
import pickle
import warnings
warnings.filterwarnings("ignore")
import matplotlib.pyplot as plt
import SDS_download, SDS_preprocess, SDS_shoreline
# define the area of interest (longitude, latitude)
polygon = [[[151.301454, -33.700754],
[151.311453, -33.702075],
[151.307237, -33.739761],
[151.294220, -33.736329],
[151.301454, -33.700754]]]
# define dates of interest
dates = ['2017-12-01', '2018-01-01']
# define satellite missions
sat_list = ['L5', 'L7', 'L8', 'S2']
# give a name to the site
sitename = 'NARRA'
# download satellite images (also saves metadata.pkl)
#SDS_download.get_images(sitename, polygon, dates, sat_list)
# load metadata structure (contains information on the downloaded satellite images and is created
# after all images have been successfully downloaded)
filepath = os.path.join(os.getcwd(), 'data', sitename)
with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'rb') as f:
metadata = pickle.load(f)
# parameters and settings
settings = {
'sitename': sitename,
# general parameters:
'cloud_thresh': 0.5, # threshold on maximum cloud cover
'output_epsg': 28356, # epsg code of the desired output spatial reference system
# shoreline detection parameters:
'min_beach_size': 20, # minimum number of connected pixels for a beach
'buffer_size': 7, # radius (in pixels) of disk for buffer around sandy pixels
'min_length_sl': 200, # minimum length of shoreline perimeter to be kept
'max_dist_ref': 100, # max distance (in meters) allowed from a reference shoreline
# quality control:
'check_detection': True # if True, shows each shoreline detection and lets the user
# decide which ones are correct and which ones are false due to
# the presence of clouds
}
# preprocess images (cloud masking, pansharpening/down-sampling)
SDS_preprocess.preprocess_all_images(metadata, settings)
# create a reference shoreline (used to identify outliers and false detections)
settings['refsl'] = SDS_preprocess.get_reference_sl(metadata, settings)
# extract shorelines from all images (also saves output.pkl)
out = SDS_shoreline.extract_shorelines(metadata, settings)
# plot shorelines
plt.figure()
plt.axis('equal')
plt.xlabel('Eastings [m]')
plt.ylabel('Northings [m]')
for satname in out.keys():
if satname == 'meta':
continue
for i in range(len(out[satname]['shoreline'])):
sl = out[satname]['shoreline'][i]
date = out[satname]['timestamp'][i]
plt.plot(sl[:, 0], sl[:, 1], '-', label=date.strftime('%d-%m-%Y'))
plt.legend()

199
shoreline_extraction.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Shoreline extraction from satellite images"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook shows how to download satellite images (Landsat 5,7,8 and Sentinel-2) from Google Earth Engine and apply the shoreline detection algorithm described in *Vos K., Harley M.D., Splinter K.D., Simmons J.A., Turner I.L. (in review). Capturing intra-annual to multi-decadal shoreline variability from publicly available satellite imagery, Coastal Engineering*. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initial settings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Python packages required to run this notebook can be installed by running the following anaconda command:\n",
"*\"conda env create -f environment.yml\"*. This will create a new enviroment with all the relevant packages installed. You will also need to sign up for Google Earth Engine (https://earthengine.google.com and go to signup) and authenticate on the computer so that python can access via your login."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pickle\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"# load modules from directory\n",
"import SDS_download, SDS_preprocess, SDS_tools, SDS_shoreline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download images"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the region of interest, the dates and the satellite missions from which you want to download images. The image will be cropped on the Google Earth Engine server and only the region of interest will be downloaded resulting in low memory allocation (~ 1 megabyte/image for 5 km of beach)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# define the area of interest (longitude, latitude)\n",
"polygon = [[[151.301454, -33.700754],\n",
" [151.311453, -33.702075], \n",
" [151.307237, -33.739761],\n",
" [151.294220, -33.736329],\n",
" [151.301454, -33.700754]]]\n",
" \n",
"# define dates of interest\n",
"dates = ['2017-12-01','2018-01-01']\n",
"\n",
"# define satellite missions ('L5' --> landsat 5 , 'S2' --> Sentinel-2)\n",
"sat_list = ['L5', 'L7', 'L8', 'S2']\n",
"\n",
"# give a name to the site\n",
"sitename = 'NARRA'\n",
"\n",
"# download satellite images. The cropped images are saved in a '/data' subfolder. The image information is stored\n",
"# into 'metadata.pkl'.\n",
"# SDS_download.get_images(sitename, polygon, dates, sat_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Shoreline extraction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Performs a sub-pixel resolution shoreline detection method integrating a supervised classification component that allows to map the boundary between water and sand."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# parameters and settings\n",
"%matplotlib qt\n",
"settings = { \n",
" 'sitename': sitename,\n",
" \n",
" # general parameters:\n",
" 'cloud_thresh': 0.5, # threshold on maximum cloud cover\n",
" 'output_epsg': 28356, # epsg code of the desired output spatial reference system\n",
" \n",
" # shoreline detection parameters:\n",
" 'min_beach_size': 20, # minimum number of connected pixels for a beach\n",
" 'buffer_size': 7, # radius (in pixels) of disk for buffer around sandy pixels\n",
" 'min_length_sl': 200, # minimum length of shoreline perimeter to be kept \n",
" 'max_dist_ref': 100 , # max distance (in meters) allowed from a reference shoreline\n",
" \n",
" # quality control:\n",
" 'check_detection': True # if True, shows each shoreline detection and lets the user \n",
" # decide which shorleines are correct and which ones are false due to\n",
" # the presence of clouds and other artefacts. \n",
" # If set to False, shorelines are extracted from all images.\n",
" }\n",
"\n",
"# load metadata structure (contains information on the downloaded satellite images and is created\n",
"# after all images have been successfully downloaded)\n",
"filepath = os.path.join(os.getcwd(), 'data', settings['sitename'])\n",
"with open(os.path.join(filepath, settings['sitename'] + '_metadata' + '.pkl'), 'rb') as f:\n",
" metadata = pickle.load(f)\n",
" \n",
"# [OPTIONAL] saves .jpg files of the preprocessed images (cloud mask and pansharpening/down-sampling) \n",
"#SDS_preprocess.preprocess_all_images(metadata, settings)\n",
"\n",
"# [OPTIONAL] to avoid false detections and identify obvious outliers there is the option to\n",
"# create a reference shoreline position (manually clicking on a satellite image)\n",
"settings['refsl'] = SDS_preprocess.get_reference_sl(metadata, settings)\n",
"\n",
"# extract shorelines from all images. Saves out.pkl which contains the shoreline coordinates for each date in the spatial\n",
"# reference system specified in settings['output_epsg']. Save the output in a file called 'out.pkl'.\n",
"out = SDS_shoreline.extract_shorelines(metadata, settings)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot the shorelines"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"plt.axis('equal')\n",
"plt.xlabel('Eastings [m]')\n",
"plt.ylabel('Northings [m]')\n",
"plt.title('Shorelines')\n",
"for satname in out.keys():\n",
" if satname == 'meta':\n",
" continue\n",
" for i in range(len(out[satname]['shoreline'])):\n",
" sl = out[satname]['shoreline'][i]\n",
" date = out[satname]['timestamp'][i]\n",
" plt.plot(sl[:,0], sl[:,1], '-', label=date.strftime('%d-%m-%Y'))\n",
"plt.legend()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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