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77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
7 years ago
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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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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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# 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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# parameters
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plot_bool = False # if you want the plots
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prob_high = 99.9 # upper probability to clip and rescale pixel intensity
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min_contour_points = 100 # minimum number of points contained in each water line
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# select collection
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input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
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# location (Narrabeen-Collaroy beach)
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rect_narra = [[[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 = '2016-01-01'
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end_date = '2016-12-01'
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# filter by location
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flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra)).filterDate(start_date, end_date)
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n_img = flt_col.size().getInfo()
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print('Number of images covering Narrabeen:', n_img)
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im_all = flt_col.getInfo().get('features')
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output = []
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# loop through all images
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for i in range(n_img):
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# find each 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, im_cloud, crs = read_eeimage(im, rect_narra, plot_bool)
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# rescale intensities
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im_ms = rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool)
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im_pan = rescale_image_intensity(im_pan, im_cloud, prob_high, plot_bool)
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# pansharpen rgb image
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im_ms_ps = pansharpen(im_ms[:,:,[0,1,2]], im_pan, im_cloud, 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 = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], im_cloud, plot_bool)
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# edge detection
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wl_pix = find_wl_contours(im_ndwi, im_cloud, min_contour_points, True)
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# convert from pixels to world coordinates
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wl_coords = convert_pix2world(wl_pix, crs['crs_15m'])
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output.append(wl_coords)
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