def bare_soil_index(raw_data, date):
low_swir = get_data.by_band_and_date(raw_data, 'B11', date)
red = get_data.by_band_and_date(raw_data, 'B04', date)
nir = get_data.by_band_and_date(raw_data, 'B08', date)
blue = get_data.by_band_and_date(raw_data, 'B02', date)
soil_index = 2.5 * ((low_swir + red) - (nir + blue)) / ((low_swir + red) +
(nir + blue))
return soil_index
def bare_soil_index(raw_data, date):
soil_index = indices.bare_soil_index(raw_data, date)
nir = get_data.by_band_and_date(raw_data, 'B08', date)
low_swir = get_data.by_band_and_date(raw_data, 'B11', date)
norm_index = get_data.normalise(soil_index)
norm_nir = get_data.normalise(nir)
norm_swir = get_data.normalise(low_swir)
image_data = np.dstack((norm_index, norm_nir, norm_swir))
render.rgb_plot(image_data)
def urban_classified(raw_data, date):
ndvi_scores = ndvi(raw_data, date)
ndmi_scores = ndmi(raw_data, date)
soil_index = bare_soil_index(raw_data, date)
low_swir = get_data.by_band_and_date(raw_data, 'B11', date)
shape = np.shape(low_swir)
classified_image = np.zeros((shape[0], shape[1], 3))
for i in range(shape[0]):
for j in range(shape[1]):
classified_image[i][j] = __urban_pixel_value(
i, j, ndvi_scores, ndmi_scores, soil_index, low_swir)
return classified_image
def ndmi(raw_data, date):
swir = get_data.by_band_and_date(raw_data, 'B11', date)
nir = get_data.by_band_and_date(raw_data, 'B08', date)
return index(nir, swir)
def gndvi(raw_data, date):
green = get_data.by_band_and_date(raw_data, 'B03', date)
nir = get_data.by_band_and_date(raw_data, 'B08', date)
return index(nir, green)
def ndvi(raw_data, date):
red = get_data.by_band_and_date(raw_data, 'B04', date)
nir = get_data.by_band_and_date(raw_data, 'B08', date)
return index(nir, red)