initial shoreline mapping
Loads images from the Google Earth Engine database and maps shorelines using the marching squares algorithmmaster
kvos
7 years ago
commit
647ef2192e
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Mar 1 14:32:08 2018
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@author: z5030440
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Main code to extract shorelines from Landsat imagery
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"""
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# Preamble
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import ee
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from IPython import display
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import math
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import matplotlib.pyplot as plt
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import numpy as np
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import pdb
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# image processing modules
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import skimage.filters as filters
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import skimage.exposure as exposure
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import skimage.transform as transform
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import sklearn.decomposition as decomposition
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import skimage.morphology as morphology
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import skimage.measure as measure
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from shapely.geometry import Polygon
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from osgeo import gdal
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from osgeo import osr
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import tempfile
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import urllib
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from urllib.request import urlretrieve
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import zipfile
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# my modules
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from utils import *
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# from sds import *
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np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
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ee.Initialize()
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plot_bool = True # if you want the plots
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def download_tif(image, bandsId):
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"""downloads tif image (region and bands) from the ee server and stores it in a temp file"""
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url = ee.data.makeDownloadUrl(ee.data.getDownloadId({
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'image': image.serialize(),
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'bands': bandsId,
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'filePerBand': 'false',
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'name': 'data',
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}))
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local_zip, headers = urlretrieve(url)
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with zipfile.ZipFile(local_zip) as local_zipfile:
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return local_zipfile.extract('data.tif', tempfile.mkdtemp())
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def load_image(image, bandsId):
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"""loads an ee.Image() as a np.array. e.Image() is retrieved from the EE database."""
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local_tif_filename = download_tif(image, bandsId)
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dataset = gdal.Open(local_tif_filename, gdal.GA_ReadOnly)
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bands = [dataset.GetRasterBand(i + 1).ReadAsArray() for i in range(dataset.RasterCount)]
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return np.stack(bands, 2), dataset
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im = ee.Image('LANDSAT/LC08/C01/T1_RT_TOA/LC08_089083_20130411')
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lon = [151.2820816040039, 151.3425064086914]
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lat = [-33.68206818063878, -33.74775138989556]
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polygon = [[lon[0], lat[0]], [lon[1], lat[0]], [lon[1], lat[1]], [lon[0], lat[1]]];
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# get image metadata into dictionnary
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im_dic = im.getInfo()
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im_bands = im_dic.get('bands')
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# delete dimensions key from dictionnary, otherwise the entire image is extracted
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#for i in range(len(im_bands)): del im_bands[i]['dimensions']
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pan_band = [im_bands[7]]
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ms_bands = [im_bands[1], im_bands[2], im_bands[3]]
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im_full, dataset_full = load_image(im, ms_bands)
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plt.figure()
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plt.imshow(np.clip(im_full[:,:,[2,1,0]] * 3, 0, 1))
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plt.show()
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#%%
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def download_tif(image, polygon, bandsId):
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"""downloads tif image (region and bands) from the ee server and stores it in a temp file"""
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url = ee.data.makeDownloadUrl(ee.data.getDownloadId({
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'image': image.serialize(),
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'region': polygon,
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'bands': bandsId,
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'filePerBand': 'false',
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'name': 'data',
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}))
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local_zip, headers = urlretrieve(url)
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with zipfile.ZipFile(local_zip) as local_zipfile:
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return local_zipfile.extract('data.tif', tempfile.mkdtemp())
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def load_image(image, polygon, bandsId):
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"""
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Loads an ee.Image() as a np.array. e.Image() is retrieved from the EE database.
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The geographic area and bands to select can be specified
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KV WRL 2018
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Arguments:
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-----------
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image: ee.Image()
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image objec from the EE database
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polygon: list
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coordinates of the points creating a polygon. Each point is a list with 2 values
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bandsId: list
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bands to select, each band is a dictionnary in the list containing the following keys:
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crs, crs_transform, data_type and id. NOTE: you have to remove the key dimensions, otherwise
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the entire image is retrieved.
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Returns:
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-----------
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image_array : np.ndarray
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An array containing the image (2D if one band, otherwise 3D)
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"""
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local_tif_filename = download_tif(image, polygon, bandsId)
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dataset = gdal.Open(local_tif_filename, gdal.GA_ReadOnly)
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bands = [dataset.GetRasterBand(i + 1).ReadAsArray() for i in range(dataset.RasterCount)]
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return np.stack(bands, 2), dataset
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for i in range(len(im_bands)): del im_bands[i]['dimensions']
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ms_bands = [im_bands[1], im_bands[2], im_bands[3]]
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im_cropped, dataset_cropped = load_image(im, polygon, ms_bands)
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plt.figure()
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plt.imshow(np.clip(im_cropped[:,:,[2,1,0]] * 3, 0, 1))
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plt.show()
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#%%
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crs_full = dataset_full.GetGeoTransform()
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crs_cropped = dataset_cropped.GetGeoTransform()
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scale = crs_full[1]
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ul_full = np.array([crs_full[0], crs_full[3]])
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ul_cropped = np.array([crs_cropped[0], crs_cropped[3]])
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delta = np.abs(ul_full - ul_cropped)/scale
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u0 = delta[0].astype('int')
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v0 = delta[1].astype('int')
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im_full[v0,u0,:]
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im_cropped[0,0,:]
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lrx = ul_cropped[0] + (dataset_cropped.RasterXSize * scale)
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lry = ul_cropped[1] + (dataset_cropped.RasterYSize * (-scale))
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lr_cropped = np.array([lrx, lry])
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delta = np.abs(ul_full - lr_cropped)/scale
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u1 = delta[0].astype('int')
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v1 = delta[1].astype('int')
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im_cropped2 = im_full[v0:v1,u0:u1,:]
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#%%
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crs_full = dataset_full.GetGeoTransform()
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source = osr.SpatialReference()
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source.ImportFromWkt(dataset_full.GetProjection())
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target = osr.SpatialReference()
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target.ImportFromEPSG(4326)
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transform = osr.CoordinateTransformation(source, target)
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transform.TransformPoint(ulx, uly)
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#%%
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crs_cropped = dataset_cropped.GetGeoTransform()
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ulx = crs_cropped[0]
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uly = crs_cropped[3]
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source = osr.SpatialReference()
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source.ImportFromWkt(dataset_cropped.GetProjection())
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target = osr.SpatialReference()
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target.ImportFromEPSG(4326)
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transform = osr.CoordinateTransformation(source, target)
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transform.TransformPoint(lrx, lry)
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#%%
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source = osr.SpatialReference()
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source.ImportFromEPSG(4326)
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target = osr.SpatialReference()
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target.ImportFromEPSG(32656)
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coords = transform.TransformPoint(151.2820816040039, -33.68206818063878)
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coords[0] - ulx
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coords[1] - uly
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#%%
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x_ul_full = ms_bands[0]['crs_transform'][2]
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y_ul_full = ms_bands[0]['crs_transform'][5]
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scale = ms_bands[0]['crs_transform'][0]
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x_ul_cropped = np.array([340756.105840223, 346357.851288875, 346474.839525944, 340877.362938763])
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y_ul_cropped = np.array([-3728229.45372866, -3728137.91775723, -3735421.58347927, -3735513.20696522])
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dx = abs(x_ul_full - x_ul_cropped)
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dy = abs(y_ul_full - y_ul_cropped)
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u_coord = np.round(dx/scale).astype('int')
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v_coord = np.round(dy/scale).astype('int')
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im_cropped2 = im_full[np.min(v_coord):np.max(v_coord), np.min(u_coord):np.max(u_coord),:]
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plt.figure()
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plt.imshow(np.clip(im_cropped2[:,:,[2,1,0]] * 3, 0, 1), cmap='gray')
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plt.show()
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sum(sum(sum(np.equal(im_cropped,im_cropped2).astype('int')-1)))
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@ -0,0 +1,519 @@
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Mar 1 11:20:35 2018
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@author: z5030440
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"""
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"""This script contains the functions needed for satellite derived shoreline (SDS) extraction"""
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# Initial settings
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import numpy as np
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import matplotlib.pyplot as plt
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import pdb
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import ee
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# other modules
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from osgeo import gdal
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import tempfile
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from urllib.request import urlretrieve
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import zipfile
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# image processing modules
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import skimage.filters as filters
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import skimage.exposure as exposure
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import skimage.transform as transform
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import sklearn.decomposition as decomposition
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import skimage.measure as measure
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# import own modules
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from utils import *
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# Download from ee server function
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def download_tif(image, polygon, bandsId):
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"""downloads tif image (region and bands) from the ee server and stores it in a temp file"""
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url = ee.data.makeDownloadUrl(ee.data.getDownloadId({
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'image': image.serialize(),
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'region': polygon,
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'bands': bandsId,
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'filePerBand': 'false',
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'name': 'data',
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}))
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local_zip, headers = urlretrieve(url)
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with zipfile.ZipFile(local_zip) as local_zipfile:
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return local_zipfile.extract('data.tif', tempfile.mkdtemp())
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def load_image(image, polygon, bandsId):
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"""
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Loads an ee.Image() as a np.array. e.Image() is retrieved from the EE database.
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The geographic area and bands to select can be specified
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KV WRL 2018
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Arguments:
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-----------
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image: ee.Image()
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image objec from the EE database
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polygon: list
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coordinates of the points creating a polygon. Each point is a list with 2 values
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bandsId: list
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bands to select, each band is a dictionnary in the list containing the following keys:
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crs, crs_transform, data_type and id. NOTE: you have to remove the key dimensions, otherwise
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the entire image is retrieved.
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Returns:
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-----------
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image_array : np.ndarray
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An array containing the image (2D if one band, otherwise 3D)
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georef : np.ndarray
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6 element vector containing the crs_parameters
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[X_ul_corner Xscale Xshear Y_ul_corner Yshear Yscale]
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"""
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local_tif_filename = download_tif(image, polygon, bandsId)
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dataset = gdal.Open(local_tif_filename, gdal.GA_ReadOnly)
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georef = np.array(dataset.GetGeoTransform())
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bands = [dataset.GetRasterBand(i + 1).ReadAsArray() for i in range(dataset.RasterCount)]
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return np.stack(bands, 2), georef
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def create_cloud_mask(im_qa):
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"""
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Creates a cloud mask from the image containing the QA band information
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KV WRL 2018
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Arguments:
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-----------
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im_qa: np.ndarray
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Image containing the QA band
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Returns:
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-----------
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cloud_mask : np.ndarray of booleans
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A boolean array with True where the cloud are present
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"""
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# convert QA bits
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cloud_values = [2800, 2804, 2808, 2812, 6896, 6900, 6904, 6908]
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cloud_mask = np.isin(im_qa, cloud_values)
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#cloud_shadow_values = [2976, 2980, 2984, 2988, 3008, 3012, 3016, 3020]
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#cloud_shadow_mask = np.isin(im_qa, cloud_shadow_values)
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return cloud_mask
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def read_eeimage(im, polygon, plot_bool):
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"""
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Read an ee.Image() object and returns the panchromatic band, multispectral bands (B, G, R, NIR, SWIR)
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and a cloud mask. All outputs are at 15m resolution (bilinear interpolation for the multispectral bands)
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KV WRL 2018
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Arguments:
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-----------
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im: ee.Image()
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Image to read from the Google Earth Engine database
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plot_bool: boolean
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True if plot is wanted
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Returns:
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-----------
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im_pan: np.ndarray (2D)
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The panchromatic band (15m)
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im_ms: np.ndarray (3D)
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The multispectral bands interpolated at 15m
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im_cloud: np.ndarray (2D)
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The cloud mask at 15m
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crs_params: list
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EPSG code and affine transformation parameters
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"""
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im_dic = im.getInfo()
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im_bands = im_dic.get('bands')
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# delete dimensions key from dictionnary, otherwise the entire image is extracted
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for i in range(len(im_bands)): del im_bands[i]['dimensions']
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# load panchromatic band
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pan_band = [im_bands[7]]
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im_pan, crs_pan = load_image(im, polygon, pan_band)
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im_pan = im_pan[:,:,0]
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# load the multispectral bands (B2,B3,B4,B5,B6) = (blue,green,red,nir,swir1)
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ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5]]
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im_ms_30m, crs_ms = load_image(im, polygon, ms_bands)
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# create cloud mask
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qa_band = [im_bands[11]]
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im_qa, crs_qa = load_image(im, polygon, qa_band)
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im_qa = im_qa[:,:,0]
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im_cloud = create_cloud_mask(im_qa)
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im_cloud = transform.resize(im_cloud, (im_pan.shape[0], im_pan.shape[1]),
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order=0, preserve_range=True, mode='constant').astype('bool_')
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if plot_bool:
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plt.figure()
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plt.subplot(221)
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plt.imshow(im_pan, cmap='gray')
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plt.title('PANCHROMATIC')
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plt.subplot(222)
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plt.imshow(im_ms_30m[:,:,[2,1,0]])
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plt.title('RGB')
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plt.subplot(223)
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plt.imshow(im_ms_30m[:,:,3], cmap='gray')
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plt.title('NIR')
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plt.subplot(224)
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plt.imshow(im_ms_30m[:,:,4], cmap='gray')
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plt.title('SWIR')
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plt.show()
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# resize the image using bilinear interpolation (order 1)
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im_ms = transform.resize(im_ms_30m,(im_pan.shape[0], im_pan.shape[1]),
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order=1, preserve_range=True, mode='constant')
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# get the crs parameters for the image at 15m and 30m resolution
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crs = {'crs_15m':crs_pan, 'crs_30m':crs_ms, 'epsg_code':pan_band[0]['crs']}
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return im_pan, im_ms, im_cloud, crs
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def rescale_image_intensity(im, cloud_mask, prob_high, plot_bool):
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"""
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Rescales the intensity of an image (multispectral or single band) by applying
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a cloud mask and clipping the prob_high upper percentile. This functions allows
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to stretch the contrast of an image.
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KV WRL 2018
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Arguments:
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-----------
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im: np.ndarray
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Image to rescale, can be 3D (multispectral) or 2D (single band)
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cloud_mask: np.ndarray
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2D cloud mask with True where cloud pixels are
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prob_high: float
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probability of exceedence used to calculate the upper percentile
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plot_bool: boolean
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True if plot is wanted
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Returns:
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-----------
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im_adj: np.ndarray
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The rescaled image
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"""
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prc_low = 0 # lower percentile
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vec_mask = cloud_mask.reshape(im.shape[0] * im.shape[1])
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if plot_bool:
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plt.figure()
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if len(im.shape) > 2:
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vec = im.reshape(im.shape[0] * im.shape[1], im.shape[2])
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vec_adj = np.ones((len(vec_mask), im.shape[2])) * np.nan
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for i in range(im.shape[2]):
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prc_high = np.percentile(vec[~vec_mask, i], prob_high)
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vec_rescaled = exposure.rescale_intensity(vec[~vec_mask, i], in_range=(prc_low, prc_high))
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vec_adj[~vec_mask,i] = vec_rescaled
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if plot_bool:
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plt.subplot(np.floor(im.shape[2]/2) + 1, np.floor(im.shape[2]/2), i+1)
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plt.hist(vec[~vec_mask, i], bins=200, label='original')
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plt.hist(vec_rescaled, bins=200, alpha=0.5, label='rescaled')
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plt.legend()
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plt.title('Band' + str(i+1))
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plt.show()
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im_adj = vec_adj.reshape(im.shape[0], im.shape[1], im.shape[2])
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if plot_bool:
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plt.figure()
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ax1 = plt.subplot(121)
|
||||
plt.imshow(im[:,:,[2,1,0]])
|
||||
plt.axis('off')
|
||||
plt.title('Original')
|
||||
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
|
||||
plt.imshow(im_adj[:,:,[2,1,0]])
|
||||
plt.axis('off')
|
||||
plt.title('Rescaled')
|
||||
plt.show()
|
||||
|
||||
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
|
||||
|
||||
if plot_bool:
|
||||
plt.hist(vec[~vec_mask], bins=200, label='original')
|
||||
plt.hist(vec_rescaled, bins=200, alpha=0.5, label='rescaled')
|
||||
plt.legend()
|
||||
plt.title('Single band')
|
||||
plt.show()
|
||||
|
||||
im_adj = vec_adj.reshape(im.shape[0], im.shape[1])
|
||||
|
||||
if plot_bool:
|
||||
plt.figure()
|
||||
ax1 = plt.subplot(121)
|
||||
plt.imshow(im, cmap='gray')
|
||||
plt.axis('off')
|
||||
plt.title('Original')
|
||||
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
|
||||
plt.imshow(im_adj, cmap='gray')
|
||||
plt.axis('off')
|
||||
plt.title('Rescaled')
|
||||
plt.show()
|
||||
|
||||
return im_adj
|
||||
|
||||
|
||||
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.ndarray
|
||||
Image to transform; the histogram is computed over the flattened
|
||||
array
|
||||
template: np.ndarray
|
||||
Template image; can have different dimensions to source
|
||||
Returns:
|
||||
-----------
|
||||
matched: np.ndarray
|
||||
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, plot_bool):
|
||||
"""
|
||||
Pansharpens a multispectral image (3D), using the panchromatic band (2D)
|
||||
and a cloud mask
|
||||
|
||||
KV WRL 2018
|
||||
|
||||
Arguments:
|
||||
-----------
|
||||
im_ms: np.ndarray
|
||||
Multispectral image to pansharpen (3D)
|
||||
im_pan: np.ndarray
|
||||
Panchromatic band (2D)
|
||||
cloud_mask: np.ndarray
|
||||
2D cloud mask with True where cloud pixels are
|
||||
plot_bool: boolean
|
||||
True if plot is wanted
|
||||
|
||||
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)
|
||||
|
||||
# normalise between 0 and 1
|
||||
for i in range(vec_pcs.shape[1]):
|
||||
vec_ms_ps[:,i] = np.divide(vec_ms_ps[:,i] - np.min(vec_ms_ps[:,i]),
|
||||
np.max(vec_ms_ps[:,i]) - np.min(vec_ms_ps[:,i]))
|
||||
# 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])
|
||||
|
||||
if plot_bool:
|
||||
plt.figure()
|
||||
ax1 = plt.subplot(121)
|
||||
plt.imshow(im_ms[:,:,[2,1,0]])
|
||||
plt.axis('off')
|
||||
plt.title('Original')
|
||||
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
|
||||
plt.imshow(im_ms_ps[:,:,[2,1,0]])
|
||||
plt.axis('off')
|
||||
plt.title('Pansharpened')
|
||||
plt.show()
|
||||
|
||||
return im_ms_ps
|
||||
|
||||
def nd_index(im1, im2, cloud_mask, plot_bool):
|
||||
"""
|
||||
Computes normalised difference index on 2 images (2D), given a cloud mask (2D)
|
||||
|
||||
KV WRL 2018
|
||||
|
||||
Arguments:
|
||||
-----------
|
||||
im1, im2: np.ndarray
|
||||
Images (2D) with which to calculate the ND index
|
||||
cloud_mask: np.ndarray
|
||||
2D cloud mask with True where cloud pixels are
|
||||
plot_bool: boolean
|
||||
True if plot is wanted
|
||||
|
||||
Returns: -----------
|
||||
im_nd: np.ndarray
|
||||
|
||||
Image (2D) containing the ND index
|
||||
"""
|
||||
vec_mask = cloud_mask.reshape(im1.shape[0] * im1.shape[1])
|
||||
vec_nd = np.ones(len(vec_mask)) * np.nan
|
||||
vec1 = im1.reshape(im1.shape[0] * im1.shape[1])
|
||||
vec2 = im2.reshape(im2.shape[0] * im2.shape[1])
|
||||
temp = np.divide(vec1[~vec_mask] - vec2[~vec_mask],
|
||||
vec1[~vec_mask] + vec2[~vec_mask])
|
||||
vec_nd[~vec_mask] = temp
|
||||
im_nd = vec_nd.reshape(im1.shape[0], im1.shape[1])
|
||||
|
||||
if plot_bool:
|
||||
plt.figure()
|
||||
plt.imshow(im_nd, cmap='seismic')
|
||||
plt.colorbar()
|
||||
plt.title('Normalised index')
|
||||
plt.show()
|
||||
|
||||
return im_nd
|
||||
|
||||
def find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool):
|
||||
"""
|
||||
Computes normalised difference index on 2 images (2D), given a cloud mask (2D)
|
||||
|
||||
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
|
||||
min_contour_points: int
|
||||
minimum number of points in each contour line
|
||||
plot_bool: boolean
|
||||
True if plot is wanted
|
||||
|
||||
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
|
||||
t_otsu = filters.threshold_otsu(vec)
|
||||
# use Marching Squares algorithm to detect contours on ndwi image
|
||||
contours = measure.find_contours(im_ndwi, t_otsu)
|
||||
# filter water lines
|
||||
contours_wl = []
|
||||
for i, contour in enumerate(contours):
|
||||
# remove contour points that are around clouds (nan values)
|
||||
if np.any(np.isnan(contour)):
|
||||
index_nan = np.where(np.isnan(contour))[0]
|
||||
contour = np.delete(contour, index_nan, axis=0)
|
||||
# remove contours that have only few points (less than min_contour_points)
|
||||
if contour.shape[0] > min_contour_points:
|
||||
contours_wl.append(contour)
|
||||
|
||||
if plot_bool:
|
||||
# plot otsu's histogram segmentation
|
||||
plt.figure()
|
||||
vals = plt.hist(vec, bins=200)
|
||||
plt.plot([t_otsu, t_otsu],[0, np.max(vals[0])], 'r-', label='Otsu threshold')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
# plot the water line contours on top of water index
|
||||
plt.figure()
|
||||
plt.imshow(im_ndwi, cmap='seismic')
|
||||
plt.colorbar()
|
||||
for i,contour in enumerate(contours_wl): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
|
||||
plt.axis('image')
|
||||
plt.title('Detected water lines')
|
||||
plt.show()
|
||||
|
||||
return contours_wl
|
||||
|
||||
def convert_pix2world(points, crs_vec):
|
||||
"""
|
||||
Converts pixel coordinates (row,columns) to world projected coordinates
|
||||
performing an affine transformation
|
||||
|
||||
KV WRL 2018
|
||||
|
||||
Arguments:
|
||||
-----------
|
||||
points: np.ndarray or list of np.ndarray
|
||||
array with 2 columns (rows first and columns second)
|
||||
crs_vec: np.ndarray
|
||||
vector of 6 elements [Xtr, Xscale, Xshear, Ytr, Yshear, Yscale]
|
||||
|
||||
Returns: -----------
|
||||
points_converted: np.ndarray or list of np.ndarray
|
||||
converted coordinates, first columns with X and second column with Y
|
||||
|
||||
"""
|
||||
|
||||
# make affine transformation matrix
|
||||
aff_mat = np.array([[crs_vec[1], crs_vec[2], crs_vec[0]],
|
||||
[crs_vec[4], crs_vec[5], crs_vec[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
|
||||
@ -0,0 +1,200 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Thu Mar 1 14:32:08 2018
|
||||
|
||||
@author: z5030440
|
||||
|
||||
Main code to extract shorelines from Landsat imagery
|
||||
"""
|
||||
# Preamble
|
||||
|
||||
import ee
|
||||
from IPython import display
|
||||
import math
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import pdb
|
||||
# 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.morphology as morphology
|
||||
# my modules
|
||||
from utils import *
|
||||
from sds1 import *
|
||||
|
||||
np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
|
||||
ee.Initialize()
|
||||
plot_bool = True
|
||||
|
||||
input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
|
||||
# filter collection on location (Narrabeen-Collaroy rectangle)
|
||||
rect_narra = [[[151.3473129272461,-33.69035274454718],
|
||||
[151.2820816040039,-33.68206818063878],
|
||||
[151.27281188964844,-33.74775138989556],
|
||||
[151.3425064086914,-33.75231878701767],
|
||||
[151.3473129272461,-33.69035274454718]]];
|
||||
flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra))
|
||||
n_img = flt_col.size().getInfo()
|
||||
print('Number of images covering Narrabeen:', n_img)
|
||||
|
||||
# select the most recent image of the filtered collection
|
||||
im = ee.Image(flt_col.sort('SENSING_TIME',False).first())
|
||||
im_dic = im.getInfo()
|
||||
image_prop = im_dic.get('properties')
|
||||
im_bands = im_dic.get('bands')
|
||||
for i in range(len(im_bands)): del im_bands[i]['dimensions'] # delete the dimensions key
|
||||
|
||||
# download the panchromatic band (B8) and QA band (Q11)
|
||||
pan_band = [im_bands[7]]
|
||||
im_pan = load_image(im, rect_narra, pan_band)
|
||||
im_pan = im_pan[:,:,0]
|
||||
size_pan = im_pan.shape
|
||||
vec_pan = im_pan.reshape(size_pan[0] * size_pan[1])
|
||||
|
||||
qa_band = [im_bands[11]]
|
||||
im_qa = load_image(im, rect_narra, qa_band)
|
||||
im_qa = im_qa[:,:,0]
|
||||
|
||||
# download the other bands (B2,B3,B4,B5,B6) = (blue,green,red,nir,swir1)
|
||||
ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5]]
|
||||
im_ms = load_image(im, rect_narra, ms_bands)
|
||||
size_ms = im_ms.shape
|
||||
vec_ms = im_ms.reshape(size_ms[0] * size_ms[1], size_ms[2])
|
||||
|
||||
# create cloud mask
|
||||
im_cloud = create_cloud_mask(im_qa)
|
||||
im_cloud_res = transform.resize(im_cloud, (size_pan[0], size_pan[1]), order=0, preserve_range=True).astype('bool_')
|
||||
vec_cloud = im_cloud.reshape(im_cloud.shape[0] * im_cloud.shape[1])
|
||||
vec_cloud_res = im_cloud_res.reshape(size_pan[0] * size_pan[1])
|
||||
|
||||
# Plot the RGB image and cloud masks
|
||||
plt.figure()
|
||||
ax1 = plt.subplot(121)
|
||||
plt.imshow(im_ms[:,:,[2,1,0]])
|
||||
plt.title('RGB')
|
||||
ax2 = plt.subplot(122)
|
||||
plt.imshow(im_cloud, cmap='gray')
|
||||
plt.title('Cloud mask')
|
||||
#ax3 = plt.subplot(133, sharex=ax1, sharey=ax1)
|
||||
#plt.imshow(im_cloud_shadow)
|
||||
#plt.title('Cloud mask shadow')
|
||||
plt.show()
|
||||
|
||||
# Resize multispectral bands (30m) to the size of the pan band (15m) using bilinear interpolation
|
||||
im_ms_res = transform.resize(im_ms,(size_pan[0], size_pan[1]), order=1, preserve_range=True, mode='constant')
|
||||
# Rescale image intensity between 0 and 1
|
||||
prob_high = 99.9
|
||||
im_ms_adj = rescale_image_intensity(im_ms_res, im_cloud_res, prob_high, plot_bool)
|
||||
im_pan_adj = rescale_image_intensity(im_pan, im_cloud_res, prob_high, plot_bool)
|
||||
|
||||
# Plot adjusted images
|
||||
plt.figure()
|
||||
plt.subplot(131)
|
||||
plt.imshow(im_pan_adj, cmap='gray')
|
||||
plt.title('PANCHROMATIC (15 m pixel)')
|
||||
|
||||
plt.subplot(132)
|
||||
plt.imshow(im_ms_adj[:,:,[2,1,0]])
|
||||
plt.title('RGB (30 m pixel)')
|
||||
plt.show()
|
||||
|
||||
plt.subplot(133)
|
||||
plt.imshow(im_ms_adj[:,:,[3,1,0]])
|
||||
plt.title('NIR-GB (30 m pixel)')
|
||||
plt.show()
|
||||
|
||||
#%% Pansharpening
|
||||
|
||||
im_ms_ps = pansharpen(im_ms_adj[:,:,[0,1,2]], im_pan_adj, im_cloud_res)
|
||||
# Add resized bands for NIR and SWIR
|
||||
im_ms_ps = np.append(im_ms_ps, im_ms_adj[:,:,[3,4]], axis=2)
|
||||
|
||||
# Plot adjusted images
|
||||
plt.figure()
|
||||
plt.subplot(121)
|
||||
plt.imshow(im_ms_adj[:,:,[2,1,0]])
|
||||
plt.title('Original RGB')
|
||||
plt.show()
|
||||
|
||||
plt.subplot(122)
|
||||
plt.imshow(im_ms_ps[:,:,[2,1,0]])
|
||||
plt.title('Pansharpened RGB')
|
||||
plt.show()
|
||||
|
||||
plt.figure()
|
||||
plt.subplot(121)
|
||||
plt.imshow(im_ms_adj[:,:,[3,1,0]])
|
||||
plt.title('Original NIR-GB')
|
||||
plt.show()
|
||||
|
||||
plt.subplot(122)
|
||||
plt.imshow(im_ms_ps[:,:,[3,1,0]])
|
||||
plt.title('Pansharpened NIR-GB')
|
||||
plt.show()
|
||||
|
||||
im_ndwi_nir = normalized_difference(im_ms_ps[:,:,3], im_ms_ps[:,:,1], im_cloud_res, plot_bool)
|
||||
vec_ndwi_nir = im_ndwi_nir.reshape(size_pan[0] * size_pan[1])
|
||||
|
||||
ndwi_nir = vec_ndwi_nir[~vec_cloud_res]
|
||||
|
||||
t_otsu = filters.threshold_otsu(ndwi_nir)
|
||||
t_min = filters.threshold_minimum(ndwi_nir)
|
||||
t_mean = filters.threshold_mean(ndwi_nir)
|
||||
t_li = filters.threshold_li(ndwi_nir)
|
||||
# try all thresholding algorithms
|
||||
|
||||
plt.figure()
|
||||
plt.hist(ndwi_nir, bins=300)
|
||||
plt.plot([t_otsu, t_otsu],[0, 15000], 'r-', label='Otsu threshold')
|
||||
#plt.plot([t_min, t_min],[0, 15000], 'g-', label='min')
|
||||
#plt.plot([t_mean, t_mean],[0, 15000], 'y-', label='mean')
|
||||
#plt.plot([t_li, t_li],[0, 15000], 'm-', label='li')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
plt.figure()
|
||||
plt.imshow(im_ndwi_nir > t_otsu, cmap='gray')
|
||||
plt.title('Binary image')
|
||||
plt.show()
|
||||
|
||||
im_bin = im_ndwi_nir > t_otsu
|
||||
im_open = morphology.binary_opening(im_bin,morphology.disk(1))
|
||||
im_close = morphology.binary_closing(im_open,morphology.disk(1))
|
||||
im_bin_coast_in = im_close ^ morphology.erosion(im_close,morphology.disk(1))
|
||||
im_bin_sl_in = morphology.remove_small_objects(im_bin_coast_in,100,8)
|
||||
|
||||
plt.figure()
|
||||
plt.subplot(121)
|
||||
plt.imshow(im_close, cmap='gray')
|
||||
plt.title('morphological closing')
|
||||
plt.subplot(122)
|
||||
plt.imshow(im_bin_sl_in, cmap='gray')
|
||||
plt.title('Water mark')
|
||||
plt.show()
|
||||
|
||||
|
||||
im_bin_coast_out = morphology.dilation(im_close,morphology.disk(1)) ^ im_close
|
||||
im_bin_sl_out = morphology.remove_small_objects(im_bin_coast_out,100,8)
|
||||
|
||||
|
||||
# Plot shorelines on top of RGB image
|
||||
im_rgb_sl = np.copy(im_ms_ps[:,:,[2,1,0]])
|
||||
|
||||
im_rgb_sl[im_bin_sl_in,0] = 0
|
||||
im_rgb_sl[im_bin_sl_in,1] = 1
|
||||
im_rgb_sl[im_bin_sl_in,2] = 1
|
||||
|
||||
im_rgb_sl[im_bin_sl_out,0] = 1
|
||||
im_rgb_sl[im_bin_sl_out,1] = 0
|
||||
im_rgb_sl[im_bin_sl_out,2] = 1
|
||||
|
||||
plt.figure()
|
||||
plt.imshow(im_rgb_sl)
|
||||
plt.title('Pansharpened RGB')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,76 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Thu Mar 1 14:32:08 2018
|
||||
|
||||
@author: z5030440
|
||||
|
||||
Main code to extract shorelines from Landsat imagery
|
||||
"""
|
||||
# Preamble
|
||||
|
||||
import ee
|
||||
import math
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import pdb
|
||||
|
||||
# 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.morphology as morphology
|
||||
import skimage.measure as measure
|
||||
|
||||
# my modules
|
||||
from utils import *
|
||||
from sds import *
|
||||
|
||||
np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
|
||||
ee.Initialize()
|
||||
|
||||
# parameters
|
||||
plot_bool = False # if you want the plots
|
||||
prob_high = 99.9 # upper probability to clip and rescale pixel intensity
|
||||
min_contour_points = 100 # minimum number of points contained in each water line
|
||||
|
||||
# select collection
|
||||
input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
|
||||
|
||||
# location (Narrabeen-Collaroy beach)
|
||||
rect_narra = [[[151.3473129272461,-33.69035274454718],
|
||||
[151.2820816040039,-33.68206818063878],
|
||||
[151.27281188964844,-33.74775138989556],
|
||||
[151.3425064086914,-33.75231878701767],
|
||||
[151.3473129272461,-33.69035274454718]]];
|
||||
# Dates
|
||||
start_date = '2016-01-01'
|
||||
end_date = '2016-12-01'
|
||||
# filter by location
|
||||
flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra)).filterDate(start_date, end_date)
|
||||
|
||||
n_img = flt_col.size().getInfo()
|
||||
print('Number of images covering Narrabeen:', n_img)
|
||||
im_all = flt_col.getInfo().get('features')
|
||||
output = []
|
||||
# loop through all images
|
||||
for i in range(n_img):
|
||||
# find each image in ee database
|
||||
im = ee.Image(im_all[i].get('id'))
|
||||
# load image as np.array
|
||||
im_pan, im_ms, im_cloud, crs = read_eeimage(im, rect_narra, plot_bool)
|
||||
# rescale intensities
|
||||
im_ms = rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool)
|
||||
im_pan = rescale_image_intensity(im_pan, im_cloud, prob_high, plot_bool)
|
||||
# pansharpen rgb image
|
||||
im_ms_ps = pansharpen(im_ms[:,:,[0,1,2]], im_pan, im_cloud, plot_bool)
|
||||
# add down-sized bands for NIR and SWIR (since pansharpening is not possible)
|
||||
im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2)
|
||||
# calculate NDWI
|
||||
im_ndwi = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], im_cloud, plot_bool)
|
||||
# edge detection
|
||||
wl_pix = find_wl_contours(im_ndwi, im_cloud, min_contour_points, True)
|
||||
# convert from pixels to world coordinates
|
||||
wl_coords = convert_pix2world(wl_pix, crs['crs_15m'])
|
||||
output.append(wl_coords)
|
||||
|
||||
@ -0,0 +1,359 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed Feb 21 18:05:01 2018
|
||||
|
||||
@author: z5030440
|
||||
"""
|
||||
|
||||
#%% Initial settings
|
||||
|
||||
# import packages
|
||||
import ee
|
||||
from IPython import display
|
||||
import math
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from osgeo import gdal
|
||||
import tempfile
|
||||
import tensorflow as tf
|
||||
import urllib
|
||||
from urllib.request import urlretrieve
|
||||
import zipfile
|
||||
import skimage.filters as filters
|
||||
import skimage.exposure as exposure
|
||||
import skimage.transform as transform
|
||||
import sklearn.decomposition as decomposition
|
||||
import skimage.morphology as morphology
|
||||
# import scripts
|
||||
from GEEImageFunctions import *
|
||||
|
||||
np.seterr(all='ignore') # raise divisions by 0 and nans
|
||||
ee.Initialize()
|
||||
|
||||
# Load image collection and filter it based on location (Narrabeen)
|
||||
|
||||
input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
|
||||
#n_img = input_col.size().getInfo()
|
||||
#print('Number of images in collection:', n_img)
|
||||
|
||||
# filter based on location (Narrabeen-Collaroy)
|
||||
rect_narra = [[[151.3473129272461,-33.69035274454718],
|
||||
[151.2820816040039,-33.68206818063878],
|
||||
[151.27281188964844,-33.74775138989556],
|
||||
[151.3425064086914,-33.75231878701767],
|
||||
[151.3473129272461,-33.69035274454718]]];
|
||||
|
||||
flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra))
|
||||
n_img = flt_col.size().getInfo()
|
||||
print('Number of images covering Narrabeen:', n_img)
|
||||
|
||||
# Select the most recent image and download it
|
||||
|
||||
im = ee.Image(flt_col.sort('SENSING_TIME',False).first())
|
||||
im_dic = im.getInfo()
|
||||
image_prop = im_dic.get('properties')
|
||||
im_bands = im_dic.get('bands')
|
||||
for i in range(len(im_bands)): del im_bands[i]['dimensions'] # delete the dimensions key
|
||||
|
||||
# download the panchromatic band (B8)
|
||||
pan_band = [im_bands[7]]
|
||||
im_pan = load_image(im, rect_narra, pan_band)
|
||||
im_pan = im_pan[:,:,0]
|
||||
size_pan = im_pan.shape
|
||||
vec_pan = im_pan.reshape(size_pan[0] * size_pan[1])
|
||||
# download the QA band (BQA)
|
||||
qa_band = [im_bands[11]]
|
||||
im_qa = load_image(im, rect_narra, qa_band)
|
||||
im_qa = im_qa[:,:,0]
|
||||
|
||||
# convert QA bits
|
||||
cloud_values = [2800, 2804, 2808, 2812, 6896, 6900, 6904, 6908]
|
||||
cloud_shadow_values = [2976, 2980, 2984, 2988, 3008, 3012, 3016, 3020]
|
||||
|
||||
# Create cloud mask (resized to be applied to the Pan band)
|
||||
im_cloud = np.isin(im_qa, cloud_values)
|
||||
im_cloud_shadow = np.isin(im_qa, cloud_shadow_values)
|
||||
im_cloud_res = transform.resize(im_cloud,(im_pan.shape[0], im_pan.shape[1]), order=0, preserve_range=True).astype('bool_')
|
||||
vec_cloud = im_cloud.reshape(im_cloud.shape[0] * im_cloud.shape[1])
|
||||
vec_cloud_res = im_cloud_res.reshape(size_pan[0] * size_pan[1])
|
||||
|
||||
|
||||
# download the other bands (B2,B3,B4,B5,B6) = (blue,green,red,nir,swir1)
|
||||
ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5]]
|
||||
im_ms = load_image(im, rect_narra, ms_bands)
|
||||
size_ms = im_ms.shape
|
||||
vec_ms = im_ms.reshape(size_ms[0] * size_ms[1], size_ms[2])
|
||||
|
||||
# Plot the RGB image and cloud masks
|
||||
plt.figure()
|
||||
ax1 = plt.subplot(121)
|
||||
plt.imshow(im_ms[:,:,[2,1,0]])
|
||||
plt.title('RGB')
|
||||
ax2 = plt.subplot(122)
|
||||
plt.imshow(im_cloud, cmap='gray')
|
||||
plt.title('Cloud mask')
|
||||
#ax3 = plt.subplot(133, sharex=ax1, sharey=ax1)
|
||||
#plt.imshow(im_cloud_shadow)
|
||||
#plt.title('Cloud mask shadow')
|
||||
plt.show()
|
||||
|
||||
# Resize multispectral bands (30m) to the size of the pan band (15m) using bilinear interpolation
|
||||
im_ms_res = transform.resize(im_ms,(size_pan[0], size_pan[1]), order=1, preserve_range=True, mode='constant')
|
||||
vec_ms_res = im_ms_res.reshape(size_pan[0] * size_pan[1], size_ms[2])
|
||||
|
||||
# Adjust intensities (set cloud pixels to 0 intensity)
|
||||
cloud_value = np.nan
|
||||
|
||||
prc_low = 0 # lower percentile
|
||||
prob_high = 99.9 # upper percentile probability to clip
|
||||
|
||||
# Rescale intensities between 0 and 1
|
||||
vec_ms_adj = np.ones((len(vec_cloud_res),size_ms[2])) * np.nan
|
||||
for i in range(im_ms.shape[2]):
|
||||
prc_high = np.percentile(vec_ms_res[~vec_cloud_res,i], prob_high)
|
||||
vec_rescaled = exposure.rescale_intensity(vec_ms_res[~vec_cloud_res,i], in_range=(prc_low,prc_high))
|
||||
plt.figure()
|
||||
plt.hist(vec_rescaled, bins = 300)
|
||||
plt.show()
|
||||
vec_ms_adj[~vec_cloud_res,i] = vec_rescaled
|
||||
|
||||
|
||||
im_ms_adj = vec_ms_adj.reshape(size_pan[0], size_pan[1], size_ms[2])
|
||||
|
||||
# same for the pan band
|
||||
vec_pan_adj = np.ones(len(vec_cloud_res)) * np.nan
|
||||
prc_high = np.percentile(vec_pan[~vec_cloud_res],prob_high)
|
||||
vec_rescaled = exposure.rescale_intensity(vec_pan[~vec_cloud_res], in_range=(prc_low,prc_high))
|
||||
plt.figure()
|
||||
plt.hist(vec_rescaled, bins = 300)
|
||||
plt.show()
|
||||
vec_pan_adj[~vec_cloud_res] = vec_rescaled
|
||||
im_pan_adj = vec_pan_adj.reshape(size_pan[0], size_pan[1])
|
||||
|
||||
# Plot adjusted images
|
||||
plt.figure()
|
||||
plt.subplot(131)
|
||||
plt.imshow(im_pan_adj, cmap='gray')
|
||||
plt.title('PANCHROMATIC (15 m pixel)')
|
||||
|
||||
plt.subplot(132)
|
||||
plt.imshow(im_ms_adj[:,:,[2,1,0]])
|
||||
plt.title('RGB (30 m pixel)')
|
||||
plt.show()
|
||||
|
||||
plt.subplot(133)
|
||||
plt.imshow(im_ms_adj[:,:,[3,1,0]])
|
||||
plt.title('NIR-GB (30 m pixel)')
|
||||
plt.show()
|
||||
|
||||
|
||||
#%% Pansharpening (PCA)
|
||||
# Run PCA on selected bands
|
||||
|
||||
sel_bands = [0,1,2]
|
||||
temp = vec_ms_adj[:,sel_bands]
|
||||
vec_ms_adj_nocloud = temp[~vec_cloud_res,:]
|
||||
pca = decomposition.PCA()
|
||||
vec_pcs = pca.fit_transform(vec_ms_adj_nocloud)
|
||||
vec_pcs_all = np.ones((len(vec_cloud_res),len(sel_bands))) * np.nan
|
||||
vec_pcs_all[~vec_cloud_res,:] = vec_pcs
|
||||
im_pcs = vec_pcs_all.reshape(size_pan[0], size_pan[1], vec_pcs.shape[1])
|
||||
|
||||
plt.figure()
|
||||
plt.subplot(221)
|
||||
plt.imshow(im_pcs[:,:,0], cmap='gray')
|
||||
plt.title('Component 1')
|
||||
plt.subplot(222)
|
||||
plt.imshow(im_pcs[:,:,1], cmap='gray')
|
||||
plt.title('Component 2')
|
||||
plt.subplot(223)
|
||||
plt.imshow(im_pcs[:,:,2], cmap='gray')
|
||||
plt.title('Component 3')
|
||||
plt.show()
|
||||
|
||||
# Compare the Pan image with the 1st Principal component
|
||||
compare_images(im_pan_adj,im_pcs[:,:,0])
|
||||
intensity_histogram(im_pan_adj)
|
||||
intensity_histogram(im_pcs[:,:,0])
|
||||
|
||||
# Match histogram of the pan image with the 1st principal component and replace the 1st component
|
||||
vec_pcs[:,0] = hist_match(vec_pan_adj[~vec_cloud_res], vec_pcs[:,0])
|
||||
vec_ms_ps = pca.inverse_transform(vec_pcs)
|
||||
|
||||
# normalise between 0 and 1
|
||||
for i in range(vec_pcs.shape[1]):
|
||||
vec_ms_ps[:,i] = np.divide(vec_ms_ps[:,i] - np.min(vec_ms_ps[:,i]),
|
||||
np.max(vec_ms_ps[:,i]) - np.min(vec_ms_ps[:,i]))
|
||||
|
||||
vec_ms_ps_all = np.ones((len(vec_cloud_res),len(sel_bands))) * np.nan
|
||||
vec_ms_ps_all[~vec_cloud_res,:] = vec_ms_ps
|
||||
im_ms_ps = vec_ms_ps_all.reshape(size_pan[0], size_pan[1], len(sel_bands))
|
||||
vec_ms_ps_all = np.append(vec_ms_ps_all, vec_ms_adj[:,[3,4]], axis=1)
|
||||
im_ms_ps = np.append(im_ms_ps, im_ms_adj[:,:,[3,4]], axis=2)
|
||||
|
||||
# Plot adjusted images
|
||||
plt.figure()
|
||||
plt.subplot(121)
|
||||
plt.imshow(im_ms_adj[:,:,[2,1,0]])
|
||||
plt.title('Original RGB')
|
||||
plt.show()
|
||||
|
||||
plt.subplot(122)
|
||||
plt.imshow(im_ms_ps[:,:,[2,1,0]])
|
||||
plt.title('Pansharpened RGB')
|
||||
plt.show()
|
||||
|
||||
plt.figure()
|
||||
plt.subplot(121)
|
||||
plt.imshow(im_ms_adj[:,:,[3,1,0]])
|
||||
plt.title('Original NIR-GB')
|
||||
plt.show()
|
||||
|
||||
plt.subplot(122)
|
||||
plt.imshow(im_ms_ps[:,:,[3,1,0]])
|
||||
plt.title('Pansharpened NIR-GB')
|
||||
plt.show()
|
||||
#%% Compute Normalized Difference Water Index (NDWI)
|
||||
|
||||
# With NIR
|
||||
vec_ndwi_nir = np.ones(len(vec_cloud_res)) * np.nan
|
||||
temp = np.divide(vec_ms_ps_all[~vec_cloud_res,3] - vec_ms_ps_all[~vec_cloud_res,1],
|
||||
vec_ms_ps_all[~vec_cloud_res,3] + vec_ms_ps_all[~vec_cloud_res,1])
|
||||
vec_ndwi_nir[~vec_cloud_res] = temp
|
||||
im_ndwi_nir = vec_ndwi_nir.reshape(size_pan[0], size_pan[1])
|
||||
|
||||
# With SWIR_1
|
||||
vec_ndwi_swir = np.ones(len(vec_cloud_res)) * np.nan
|
||||
temp = np.divide(vec_ms_ps_all[~vec_cloud_res,4] - vec_ms_ps_all[~vec_cloud_res,1],
|
||||
vec_ms_ps_all[~vec_cloud_res,4] + vec_ms_ps_all[~vec_cloud_res,1])
|
||||
vec_ndwi_swir[~vec_cloud_res] = temp
|
||||
im_ndwi_swir = vec_ndwi_swir.reshape(size_pan[0], size_pan[1])
|
||||
|
||||
plt.figure()
|
||||
ax1 = plt.subplot(211)
|
||||
plt.hist(vec_ndwi_nir[~vec_cloud_res], bins=300, label='NIR')
|
||||
plt.hist(vec_ndwi_swir[~vec_cloud_res], bins=300, label='SWIR', alpha=0.5)
|
||||
plt.legend()
|
||||
ax2 = plt.subplot(212, sharex=ax1)
|
||||
plt.hist(vec_ndwi_nir[~vec_cloud_res], bins=300, cumulative=True, histtype='step', label='NIR')
|
||||
plt.hist(vec_ndwi_swir[~vec_cloud_res], bins=300, cumulative=True, histtype='step', label='SWIR')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
compare_images(im_ndwi_nir,im_ndwi_swir)
|
||||
|
||||
plt.figure()
|
||||
plt.imshow(im_ndwi_nir, cmap='seismic')
|
||||
plt.title('Water Index')
|
||||
plt.colorbar()
|
||||
plt.show()
|
||||
|
||||
#%% Extract shorelines (NIR)
|
||||
|
||||
ndwi_nir = vec_ndwi_nir[~vec_cloud_res]
|
||||
|
||||
t_otsu = filters.threshold_otsu(ndwi_nir)
|
||||
t_min = filters.threshold_minimum(ndwi_nir)
|
||||
t_mean = filters.threshold_mean(ndwi_nir)
|
||||
t_li = filters.threshold_li(ndwi_nir)
|
||||
# try all thresholding algorithms
|
||||
|
||||
plt.figure()
|
||||
plt.hist(ndwi_nir, bins=300)
|
||||
plt.plot([t_otsu, t_otsu],[0, 15000], 'r-', label='Otsu threshold')
|
||||
#plt.plot([t_min, t_min],[0, 15000], 'g-', label='min')
|
||||
#plt.plot([t_mean, t_mean],[0, 15000], 'y-', label='mean')
|
||||
#plt.plot([t_li, t_li],[0, 15000], 'm-', label='li')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
plt.figure()
|
||||
plt.imshow(im_ndwi_nir > t_otsu, cmap='gray')
|
||||
plt.title('Binary image')
|
||||
plt.show()
|
||||
|
||||
im_bin = im_ndwi_nir > t_otsu
|
||||
im_open = morphology.binary_opening(im_bin,morphology.disk(1))
|
||||
im_close = morphology.binary_closing(im_open,morphology.disk(1))
|
||||
im_bin_coast_in = im_close ^ morphology.erosion(im_close,morphology.disk(1))
|
||||
im_bin_sl_in = morphology.remove_small_objects(im_bin_coast_in,100,8)
|
||||
|
||||
compare_images(im_close,im_bin_sl_in)
|
||||
|
||||
plt.figure()
|
||||
plt.subplot(121)
|
||||
plt.imshow(im_close, cmap='gray')
|
||||
plt.title('morphological closing')
|
||||
plt.subplot(122)
|
||||
plt.imshow(im_bin_sl_in, cmap='gray')
|
||||
plt.title('Water mark')
|
||||
plt.show()
|
||||
|
||||
|
||||
im_bin_coast_out = morphology.dilation(im_close,morphology.disk(1)) ^ im_close
|
||||
im_bin_sl_out = morphology.remove_small_objects(im_bin_coast_out,100,8)
|
||||
|
||||
|
||||
# Plot shorelines on top of RGB image
|
||||
im_rgb_sl = np.copy(im_ms_ps[:,:,[2,1,0]])
|
||||
|
||||
im_rgb_sl[im_bin_sl_in,0] = 0
|
||||
im_rgb_sl[im_bin_sl_in,1] = 1
|
||||
im_rgb_sl[im_bin_sl_in,2] = 1
|
||||
|
||||
im_rgb_sl[im_bin_sl_out,0] = 1
|
||||
im_rgb_sl[im_bin_sl_out,1] = 0
|
||||
im_rgb_sl[im_bin_sl_out,2] = 1
|
||||
|
||||
plt.figure()
|
||||
plt.imshow(im_rgb_sl)
|
||||
plt.title('Pansharpened RGB')
|
||||
plt.show()
|
||||
|
||||
#%% Extract shorelines SWIR
|
||||
|
||||
ndwi_swir = vec_ndwi_swir[~vec_cloud_res]
|
||||
t_otsu = filters.threshold_otsu(ndwi_swir)
|
||||
|
||||
plt.figure()
|
||||
plt.hist(ndwi_swir, bins=300)
|
||||
plt.plot([t_otsu, t_otsu],[0, 15000], 'r-', label='Otsu threshold')
|
||||
#plt.plot([t_min, t_min],[0, 15000], 'g-', label='min')
|
||||
#plt.plot([t_mean, t_mean],[0, 15000], 'y-', label='mean')
|
||||
#plt.plot([t_li, t_li],[0, 15000], 'm-', label='li')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
plt.figure()
|
||||
plt.imshow(im_ndwi_swir > t_otsu, cmap='gray')
|
||||
plt.title('Binary image')
|
||||
plt.show()
|
||||
|
||||
im_bin = im_ndwi_swir > t_otsu
|
||||
im_open = morphology.binary_opening(im_bin,morphology.disk(1))
|
||||
im_close = morphology.binary_closing(im_open,morphology.disk(1))
|
||||
im_bin_coast_in = im_close ^ morphology.erosion(im_close,morphology.disk(1))
|
||||
im_bin_sl_in = morphology.remove_small_objects(im_bin_coast_in,100,8)
|
||||
|
||||
compare_images(im_close,im_bin_sl_in)
|
||||
|
||||
|
||||
im_bin_coast_out = morphology.dilation(im_close,morphology.disk(1)) ^ im_close
|
||||
im_bin_sl_out = morphology.remove_small_objects(im_bin_coast_out,100,8)
|
||||
|
||||
|
||||
# Plot shorelines on top of RGB image
|
||||
im_rgb_sl = np.copy(im_ms_ps[:,:,[2,1,0]])
|
||||
|
||||
im_rgb_sl[im_bin_sl_in,0] = 0
|
||||
im_rgb_sl[im_bin_sl_in,1] = 1
|
||||
im_rgb_sl[im_bin_sl_in,2] = 1
|
||||
|
||||
im_rgb_sl[im_bin_sl_out,0] = 1
|
||||
im_rgb_sl[im_bin_sl_out,1] = 0
|
||||
im_rgb_sl[im_bin_sl_out,2] = 1
|
||||
|
||||
plt.figure()
|
||||
plt.imshow(im_rgb_sl)
|
||||
plt.title('Pansharpened RGB')
|
||||
plt.show()
|
||||
@ -0,0 +1,54 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Thu Mar 1 11:30:31 2018
|
||||
|
||||
@author: z5030440
|
||||
|
||||
Contains all the utilities, convenience functions and small functions that do simple things
|
||||
"""
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
|
||||
|
||||
def ecdf(x):
|
||||
"""convenience function for computing the empirical CDF"""
|
||||
vals, counts = np.unique(x, return_counts=True)
|
||||
ecdf = np.cumsum(counts).astype(np.float64)
|
||||
ecdf /= ecdf[-1]
|
||||
return vals, ecdf
|
||||
|
||||
def intensity_histogram(image):
|
||||
"""plots histogram and cumulative distribution of the pixel intensities in an image"""
|
||||
imSize = image.shape
|
||||
if len(imSize) == 2:
|
||||
im = image[:,:].reshape(imSize[0] * imSize[1])
|
||||
im = im[~np.isnan(im)]
|
||||
fig, (ax1, ax2) = plt.subplots(2,1, sharex=True, figsize = (8,6))
|
||||
ax1.hist(im, bins=300)
|
||||
ax1.set_title('Probability density function')
|
||||
ax2.hist(im, bins=300, cumulative=True, histtype='step')
|
||||
ax2.set_title('Cumulative distribution')
|
||||
plt.show()
|
||||
|
||||
else:
|
||||
for i in range(imSize[2]):
|
||||
im = image[:,:,i].reshape(imSize[0] * imSize[1])
|
||||
im = im[~np.isnan(im)]
|
||||
fig, (ax1, ax2) = plt.subplots(2,1, sharex=True, figsize = (8,6))
|
||||
ax1.hist(im, bins=300)
|
||||
ax1.set_title('Probability density function')
|
||||
ax2.hist(im, bins=300, cumulative=True, histtype='step')
|
||||
ax2.set_title('Cumulative distribution')
|
||||
plt.show()
|
||||
|
||||
def compare_images(im1, im2):
|
||||
"""plots 2 images next to each other, sharing the axis"""
|
||||
plt.figure()
|
||||
ax1 = plt.subplot(121)
|
||||
plt.imshow(im1, cmap='gray')
|
||||
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
|
||||
plt.imshow(im2, cmap='gray')
|
||||
plt.show()
|
||||
|
||||
Loading…
Reference in New Issue