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81 lines
2.9 KiB
Python
81 lines
2.9 KiB
Python
#==========================================================#
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# Shoreline extraction from satellite images
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#==========================================================#
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# load modules
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import os
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import pickle
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import warnings
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warnings.filterwarnings("ignore")
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import matplotlib.pyplot as plt
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import SDS_download, SDS_preprocess, SDS_shoreline
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# define the area of interest (longitude, latitude)
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polygon = [[[151.301454, -33.700754],
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[151.311453, -33.702075],
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[151.307237, -33.739761],
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[151.294220, -33.736329],
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[151.301454, -33.700754]]]
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# define dates of interest
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dates = ['2017-12-01', '2018-01-01']
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# define satellite missions
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sat_list = ['L5', 'L7', 'L8', 'S2']
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# give a name to the site
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sitename = 'NARRA'
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# download satellite images (also saves metadata.pkl)
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#SDS_download.get_images(sitename, polygon, dates, sat_list)
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# load metadata structure (contains information on the downloaded satellite images and is created
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# after all images have been successfully downloaded)
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filepath = os.path.join(os.getcwd(), 'data', sitename)
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with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'rb') as f:
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metadata = pickle.load(f)
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# parameters and settings
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settings = {
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'sitename': sitename,
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# general parameters:
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'cloud_thresh': 0.5, # threshold on maximum cloud cover
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'output_epsg': 28356, # epsg code of the desired output spatial reference system
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# shoreline detection parameters:
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'min_beach_size': 20, # minimum number of connected pixels for a beach
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'buffer_size': 7, # radius (in pixels) of disk for buffer around sandy pixels
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'min_length_sl': 200, # minimum length of shoreline perimeter to be kept
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'max_dist_ref': 100, # max distance (in meters) allowed from a reference shoreline
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# quality control:
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'check_detection': True # if True, shows each shoreline detection and lets the user
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# decide which ones are correct and which ones are false due to
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# the presence of clouds
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}
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# preprocess images (cloud masking, pansharpening/down-sampling)
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SDS_preprocess.preprocess_all_images(metadata, settings)
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# create a reference shoreline (used to identify outliers and false detections)
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settings['refsl'] = SDS_preprocess.get_reference_sl(metadata, settings)
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# extract shorelines from all images (also saves output.pkl)
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out = SDS_shoreline.extract_shorelines(metadata, settings)
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# plot shorelines
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plt.figure()
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plt.axis('equal')
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plt.xlabel('Eastings [m]')
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plt.ylabel('Northings [m]')
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for satname in out.keys():
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if satname == 'meta':
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continue
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for i in range(len(out[satname]['shoreline'])):
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sl = out[satname]['shoreline'][i]
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date = out[satname]['timestamp'][i]
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plt.plot(sl[:, 0], sl[:, 1], '-', label=date.strftime('%d-%m-%Y'))
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plt.legend()
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