141 lines
4.6 KiB
Python
141 lines
4.6 KiB
Python
# -*- coding: utf-8 -*-
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"""
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HyTools: Hyperspectral image processing library
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Copyright (C) 2021 University of Wisconsin
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Authors: Adam Chlus, Zhiwei Ye, Philip Townsend.
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, version 3 of the License.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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This module contains functions to apply the Modified topographic correction (SCS+C)
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described in the following paper:
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Richter, R., Kellenberger, T., & Kaufmann, H. (2009).
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Comparison of topographic correction methods.
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Remote Sensing, 1(3), 184-196.
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https://doi.org/10.3390/rs1030184
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Topographic correction consists of the following steps:
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"""
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import numpy as np
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def calc_modminn_coeffs(hy_obj,topo_dict):
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'''
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Args:
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hy_obj (TYPE): DESCRIPTION.
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Returns:
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None.
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'''
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hy_obj.topo =topo_dict
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cos_i = hy_obj.cosine_i()
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i = np.rad2deg(np.arccos(cos_i))
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solar_zn = hy_obj.get_anc('solar_zn',radians=False)
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solar_zn_t = np.zeros(solar_zn.shape)
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solar_zn_t[solar_zn < 45] = solar_zn[solar_zn < 45] +20
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solar_zn_t[(solar_zn >= 45) & (solar_zn <= 55)] = solar_zn[(solar_zn >= 45) & (solar_zn <= 55)] +15
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solar_zn_t[solar_zn > 55] = solar_zn[solar_zn > 55] +10
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#Create NDVI mask to seperate vegetation
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ir = hy_obj.get_wave(850)
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red = hy_obj.get_wave(660)
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ndvi = (ir-red)/(ir+red)
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veg_mask = ndvi > 0.2
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c_factors = np.ones((2,hy_obj.lines,hy_obj.columns))
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c_factors[:] = cos_i/np.cos(np.radians(solar_zn_t))
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# Non vegetation correction factor
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c_factors[0][~veg_mask] = c_factors[0][~veg_mask]**(1/2)
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c_factors[1][~veg_mask] = c_factors[1][~veg_mask]**(1/2)
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# Vegetation correction factor
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c_factors[0][veg_mask] = c_factors[0][veg_mask]**(3/4)
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c_factors[1][veg_mask] = c_factors[1][veg_mask]**(1/3)
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#Adjust correction factors to prevent too strong correction
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c_factors[c_factors <.25] = .25
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c_factors[c_factors > 1] = 1
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#Correct pixels only where i > threshold
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c_factors[0][i < solar_zn_t] = 1
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c_factors[1][i < solar_zn_t] = 1
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c_factors[0][ir == hy_obj.no_data] = 1
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c_factors[1][ir == hy_obj.no_data] = 1
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hy_obj.ancillary['mm_c_factor'] = c_factors
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def apply_modminn(hy_obj,data,dimension,index):
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''' Apply SCSS correction to a slice of the data
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Args:
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hy_obj (TYPE): DESCRIPTION.
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band (TYPE): DESCRIPTION.
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index (TYPE): DESCRIPTION.
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Returns:
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band (TYPE): DESCRIPTION.
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'''
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if 'mm_c_factor' not in hy_obj.ancillary.keys():
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calc_modminn_coeffs(hy_obj)
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#Convert to float
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data = data.astype(np.float32)
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wave_mask =hy_obj.wavelengths >=720
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if dimension == 'line':
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#index= 3000
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#data = hy_obj.get_line(3000)
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data[:,wave_mask] = data[:,wave_mask]*hy_obj.ancillary['mm_c_factor'][1,index,:][:,np.newaxis]
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data[:,~wave_mask] = data[:,~wave_mask]*hy_obj.ancillary['mm_c_factor'][0,index,:][:,np.newaxis]
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elif dimension == 'column':
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#index= 300
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#data = hy_obj.get_column(index)
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data[:,wave_mask] = data[:,wave_mask]*hy_obj.ancillary['mm_c_factor'][1,:,index][:,np.newaxis]
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data[:,~wave_mask] = data[:,~wave_mask]*hy_obj.ancillary['mm_c_factor'][0,:,index][:,np.newaxis]
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elif dimension == 'band':
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#index= 50
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#data = hy_obj.get_band(index)
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if hy_obj.wavelengths[index] >=720:
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cf_index = 1
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else:
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cf_index = 0
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data = data * hy_obj.ancillary['mm_c_factor'][cf_index]
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elif dimension == 'chunk':
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#index = 200,501,3000,3501
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x1,x2,y1,y2 = index
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#data = hy_obj.get_chunk(x1,x2,y1,y2)
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data[:,:,wave_mask] = data[:,:,wave_mask]*hy_obj.ancillary['mm_c_factor'][1,y1:y2,x1:x2][:,:,np.newaxis]
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data[:,:,~wave_mask] = data[:,:,~wave_mask]*hy_obj.ancillary['mm_c_factor'][0,y1:y2,x1:x2][:,:,np.newaxis]
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elif dimension == 'pixels':
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#index = [[2000,2001],[200,501]]
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y,x = index
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#data = hy_obj.get_pixels(y,x)
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data[:,wave_mask] = data[:,wave_mask]*hy_obj.ancillary['mm_c_factor'][1,y,x][:, np.newaxis]
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data[:,~wave_mask] = data[:,~wave_mask]*hy_obj.ancillary['mm_c_factor'][0,y,x][:, np.newaxis]
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return data
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