forked from kilianv/CoastSat_WRL
added gitignore file
parent
eecdb485fc
commit
783cd5d033
@ -0,0 +1,2 @@
|
||||
*.pyc
|
||||
*.mat
|
@ -0,0 +1,80 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Mon Mar 19 14:44:57 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
|
||||
# my functions
|
||||
from functions.utils import *
|
||||
from functions.sds import *
|
||||
|
||||
np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
|
||||
ee.Initialize()
|
||||
|
||||
# parameters
|
||||
plot_bool = True # 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_TOA')
|
||||
|
||||
# location (Narrabeen-Collaroy beach)
|
||||
rect_narra = [[[151.317395,-33.494601],
|
||||
[151.388635,-33.495174],
|
||||
[151.363624,-33.565184],
|
||||
[151.305228,-33.563299],
|
||||
[151.317395,-33.494601]]];
|
||||
|
||||
# filter by location
|
||||
flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra))
|
||||
|
||||
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
|
||||
# find each image in ee database
|
||||
i = 2
|
||||
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)
|
||||
|
||||
plt.figure()
|
||||
plt.imshow(im_ms_ps[:,:,[2,1,0]])
|
||||
plt.axis('off')
|
||||
plt.title('RGB at 15m')
|
||||
plt.show()
|
Loading…
Reference in New Issue