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import numpy as np
import scipy
import scipy.signal as scisig
def rescale(data, limits):
return (data - limits[0]) / (limits[1] - limits[0])
def normalized_difference(channel1, channel2):
subchan = channel1 - channel2
sumchan = channel1 + channel2
sumchan[sumchan == 0] = 0.001 # checking for 0 divisions
return subchan / sumchan
def get_shadow_mask(data_image):
data_image = data_image / 10000.
(ch, r, c) = data_image.shape
shadowmask = np.zeros((r, c)).astype('float32')
BB = data_image[1]
BNIR = data_image[7]
BSWIR1 = data_image[11]
CSI = (BNIR + BSWIR1) / 2.
t3 = 3/4 # cloud-score index threshold
T3 = np.min(CSI) + t3 * (np.mean(CSI) - np.min(CSI))
t4 = 5 / 6 # water-body index threshold
T4 = np.min(BB) + t4 * (np.mean(BB) - np.min(BB))
shadow_tf = np.logical_and(CSI < T3, BB < T4)
shadowmask[shadow_tf] = -1
shadowmask = scisig.medfilt2d(shadowmask, 5)
return shadowmask
def get_cloud_mask(data_image, cloud_threshold, binarize=False, use_moist_check=False):
'''Adapted from https://github.com/samsammurphy/cloud-masking-sentinel2/blob/master/cloud-masking-sentinel2.ipynb'''
data_image = data_image / 10000.
(ch, r, c) = data_image.shape
# Cloud until proven otherwise
score = np.ones((r, c)).astype('float32')
# Clouds are reasonably bright in the blue and aerosol/cirrus bands.
score = np.minimum(score, rescale(data_image[1], [0.1, 0.5]))
score = np.minimum(score, rescale(data_image[0], [0.1, 0.3]))
score = np.minimum(score, rescale((data_image[0] + data_image[10]), [0.4, 0.9]))
score = np.minimum(score, rescale((data_image[3] + data_image[2] + data_image[1]), [0.2, 0.8]))
if use_moist_check:
# Clouds are moist
ndmi = normalized_difference(data_image[7], data_image[11])
score = np.minimum(score, rescale(ndmi, [-0.1, 0.1]))
# However, clouds are not snow.
ndsi = normalized_difference(data_image[2], data_image[11])
score = np.minimum(score, rescale(ndsi, [0.8, 0.6]))
boxsize = 7
box = np.ones((boxsize, boxsize)) / (boxsize ** 2)
score = scipy.ndimage.morphology.grey_closing(score, size=(5, 5))
score = scisig.convolve2d(score, box, mode='same')
score = np.clip(score, 0.00001, 1.0)
if binarize:
score[score >= cloud_threshold] = 1
score[score < cloud_threshold] = 0
return score
# IN: [13 x H x W] S2 image (of arbitrary resolution H,W), scalar cloud detection threshold
# OUT: cloud & shadow segmentation mask (of same resolution)
# the multispectral S2 images are expected to have their default ranges and not be value-standardized yet
# cloud_threshold: the higher the more conservative the masks (i.e. less pixels labeled clouds/shadows)
def get_cloud_cloudshadow_mask(data_image, cloud_threshold):
cloud_mask = get_cloud_mask(data_image, cloud_threshold, binarize=True)
shadow_mask = get_shadow_mask(data_image)
# encode clouds and shadows as segmentation masks
cloud_cloudshadow_mask = np.zeros_like(cloud_mask)
cloud_cloudshadow_mask[shadow_mask < 0] = -1
cloud_cloudshadow_mask[cloud_mask > 0] = 1
return cloud_cloudshadow_mask
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