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111 lines
3.7 KiB
Python
111 lines
3.7 KiB
Python
# -*- coding: utf-8 -*-
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#==========================================================#
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# Extract shorelines from Landsat images
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#==========================================================#
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# Initial settings
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import ee
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import matplotlib.pyplot as plt
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import matplotlib.cm as cm
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import numpy as np
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import pandas as pd
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from datetime import datetime
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import pickle
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import pdb
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import pytz
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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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# my modules
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import functions.utils as utils
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import functions.sds as sds
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# some settings
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np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
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plt.rcParams['axes.grid'] = False
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plt.rcParams['figure.max_open_warning'] = 100
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ee.Initialize()
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# parameters
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cloud_thresh = 0.5 # threshold for cloud cover
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plot_bool = True # if you want the plots
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min_contour_points = 100# minimum number of points contained in each water line
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output_epsg = 28356 # GDA94 / MGA Zone 56
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buffer_size = 10 # radius of disk for buffer (sand classif parameter)
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min_beach_size = 50 # number of pixels in a beach (sand classif parameter)
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# select collection
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satname = 'L8'
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input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA') # Landsat 8 Tier 1 TOA
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# location (Narrabeen-Collaroy beach)
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polygon = [[[151.3473129272461,-33.69035274454718],
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[151.2820816040039,-33.68206818063878],
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[151.27281188964844,-33.74775138989556],
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[151.3425064086914,-33.75231878701767],
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[151.3473129272461,-33.69035274454718]]];
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# dates
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start_date = '2013-01-01'
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end_date = '2018-12-31'
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# filter by location and date
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flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(start_date, end_date)
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n_img = flt_col.size().getInfo()
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print('Number of images covering the polygon:', n_img)
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im_all = flt_col.getInfo().get('features')
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i = 0 # first image
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# find image in ee database
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im = ee.Image(im_all[i].get('id'))
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# load image as np.array
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im_pan, im_ms, cloud_mask, crs, meta = sds.read_eeimage(im, polygon, satname, plot_bool)
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# mask -inf or nan values on the image and add to cloud_mask
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im_inf = np.isin(im_ms[:,:,0], -np.inf)
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im_nan = np.isnan(im_ms[:,:,0])
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cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
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cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
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print('Cloud cover : ' + str(int(round(100*cloud_cover))) + ' %')
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# pansharpen rgb image
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im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, plot_bool)
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# add down-sized bands for NIR and SWIR (since pansharpening is not possible)
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im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2)
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# calculate NDWI
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im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, plot_bool)
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# edge detection
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wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool)
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plt.figure()
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plt.imshow(im_ms_ps[:,:,[2,1,0]])
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for i,contour in enumerate(wl_pix): plt.plot(contour[:, 1], contour[:, 0], linewidth=2)
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plt.axis('image')
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plt.title('Detected water lines')
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plt.show()
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# convert from pixels to world coordinates
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wl_coords = sds.convert_pix2world(wl_pix, crs['crs_15m'])
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# convert to output epsg spatial reference
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wl = sds.convert_epsg(wl_coords, crs['epsg_code'], output_epsg)
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# classify sand pixels with Kmeans
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#im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool)
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# classify image in 4 classes (sand, whitewater, water, other) with NN classifier
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im_classif = sds.classify_image_NN(im_ms_ps, im_pan, cloud_mask, plot_bool) |