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201 lines
5.7 KiB
Python
201 lines
5.7 KiB
Python
# -*- 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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# my modules
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from utils import *
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from sds1 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
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input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
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# filter collection on location (Narrabeen-Collaroy rectangle)
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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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flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra))
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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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# select the most recent image of the filtered collection
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im = ee.Image(flt_col.sort('SENSING_TIME',False).first())
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im_dic = im.getInfo()
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image_prop = im_dic.get('properties')
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im_bands = im_dic.get('bands')
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for i in range(len(im_bands)): del im_bands[i]['dimensions'] # delete the dimensions key
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# download the panchromatic band (B8) and QA band (Q11)
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pan_band = [im_bands[7]]
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im_pan = load_image(im, rect_narra, pan_band)
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im_pan = im_pan[:,:,0]
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size_pan = im_pan.shape
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vec_pan = im_pan.reshape(size_pan[0] * size_pan[1])
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qa_band = [im_bands[11]]
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im_qa = load_image(im, rect_narra, qa_band)
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im_qa = im_qa[:,:,0]
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# download the other 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 = load_image(im, rect_narra, ms_bands)
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size_ms = im_ms.shape
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vec_ms = im_ms.reshape(size_ms[0] * size_ms[1], size_ms[2])
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# create cloud mask
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im_cloud = create_cloud_mask(im_qa)
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im_cloud_res = transform.resize(im_cloud, (size_pan[0], size_pan[1]), order=0, preserve_range=True).astype('bool_')
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vec_cloud = im_cloud.reshape(im_cloud.shape[0] * im_cloud.shape[1])
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vec_cloud_res = im_cloud_res.reshape(size_pan[0] * size_pan[1])
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# Plot the RGB image and cloud masks
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plt.figure()
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ax1 = plt.subplot(121)
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plt.imshow(im_ms[:,:,[2,1,0]])
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plt.title('RGB')
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ax2 = plt.subplot(122)
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plt.imshow(im_cloud, cmap='gray')
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plt.title('Cloud mask')
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#ax3 = plt.subplot(133, sharex=ax1, sharey=ax1)
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#plt.imshow(im_cloud_shadow)
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#plt.title('Cloud mask shadow')
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plt.show()
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# Resize multispectral bands (30m) to the size of the pan band (15m) using bilinear interpolation
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im_ms_res = transform.resize(im_ms,(size_pan[0], size_pan[1]), order=1, preserve_range=True, mode='constant')
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# Rescale image intensity between 0 and 1
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prob_high = 99.9
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im_ms_adj = rescale_image_intensity(im_ms_res, im_cloud_res, prob_high, plot_bool)
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im_pan_adj = rescale_image_intensity(im_pan, im_cloud_res, prob_high, plot_bool)
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# Plot adjusted images
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plt.figure()
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plt.subplot(131)
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plt.imshow(im_pan_adj, cmap='gray')
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plt.title('PANCHROMATIC (15 m pixel)')
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plt.subplot(132)
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plt.imshow(im_ms_adj[:,:,[2,1,0]])
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plt.title('RGB (30 m pixel)')
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plt.show()
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plt.subplot(133)
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plt.imshow(im_ms_adj[:,:,[3,1,0]])
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plt.title('NIR-GB (30 m pixel)')
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plt.show()
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#%% Pansharpening
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im_ms_ps = pansharpen(im_ms_adj[:,:,[0,1,2]], im_pan_adj, im_cloud_res)
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# Add resized bands for NIR and SWIR
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im_ms_ps = np.append(im_ms_ps, im_ms_adj[:,:,[3,4]], axis=2)
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# Plot adjusted images
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plt.figure()
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plt.subplot(121)
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plt.imshow(im_ms_adj[:,:,[2,1,0]])
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plt.title('Original RGB')
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plt.show()
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plt.subplot(122)
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plt.imshow(im_ms_ps[:,:,[2,1,0]])
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plt.title('Pansharpened RGB')
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plt.show()
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plt.figure()
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plt.subplot(121)
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plt.imshow(im_ms_adj[:,:,[3,1,0]])
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plt.title('Original NIR-GB')
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plt.show()
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plt.subplot(122)
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plt.imshow(im_ms_ps[:,:,[3,1,0]])
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plt.title('Pansharpened NIR-GB')
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plt.show()
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im_ndwi_nir = normalized_difference(im_ms_ps[:,:,3], im_ms_ps[:,:,1], im_cloud_res, plot_bool)
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vec_ndwi_nir = im_ndwi_nir.reshape(size_pan[0] * size_pan[1])
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ndwi_nir = vec_ndwi_nir[~vec_cloud_res]
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t_otsu = filters.threshold_otsu(ndwi_nir)
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t_min = filters.threshold_minimum(ndwi_nir)
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t_mean = filters.threshold_mean(ndwi_nir)
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t_li = filters.threshold_li(ndwi_nir)
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# try all thresholding algorithms
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plt.figure()
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plt.hist(ndwi_nir, bins=300)
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plt.plot([t_otsu, t_otsu],[0, 15000], 'r-', label='Otsu threshold')
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#plt.plot([t_min, t_min],[0, 15000], 'g-', label='min')
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#plt.plot([t_mean, t_mean],[0, 15000], 'y-', label='mean')
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#plt.plot([t_li, t_li],[0, 15000], 'm-', label='li')
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plt.legend()
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plt.show()
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plt.figure()
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plt.imshow(im_ndwi_nir > t_otsu, cmap='gray')
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plt.title('Binary image')
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plt.show()
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im_bin = im_ndwi_nir > t_otsu
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im_open = morphology.binary_opening(im_bin,morphology.disk(1))
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im_close = morphology.binary_closing(im_open,morphology.disk(1))
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im_bin_coast_in = im_close ^ morphology.erosion(im_close,morphology.disk(1))
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im_bin_sl_in = morphology.remove_small_objects(im_bin_coast_in,100,8)
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plt.figure()
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plt.subplot(121)
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plt.imshow(im_close, cmap='gray')
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plt.title('morphological closing')
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plt.subplot(122)
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plt.imshow(im_bin_sl_in, cmap='gray')
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plt.title('Water mark')
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plt.show()
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im_bin_coast_out = morphology.dilation(im_close,morphology.disk(1)) ^ im_close
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im_bin_sl_out = morphology.remove_small_objects(im_bin_coast_out,100,8)
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# Plot shorelines on top of RGB image
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im_rgb_sl = np.copy(im_ms_ps[:,:,[2,1,0]])
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im_rgb_sl[im_bin_sl_in,0] = 0
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im_rgb_sl[im_bin_sl_in,1] = 1
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im_rgb_sl[im_bin_sl_in,2] = 1
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im_rgb_sl[im_bin_sl_out,0] = 1
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im_rgb_sl[im_bin_sl_out,1] = 0
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im_rgb_sl[im_bin_sl_out,2] = 1
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plt.figure()
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plt.imshow(im_rgb_sl)
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plt.title('Pansharpened RGB')
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plt.show()
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