# -*- coding: utf-8 -*- """ HyTools: Hyperspectral image processing library Copyright (C) 2021 University of Wisconsin Authors: Adam Chlus, Zhiwei Ye, Philip Townsend. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . This module contains functions to apply a topographic correction (SCS+C) described in the following papers: Scott A. Soenen, Derek R. Peddle, & Craig A. Coburn (2005). SCS+C: A Modified Sun-Canopy-Sensor Topographic Correction in Forested Terrain. IEEE Transactions on Geoscience and Remote Sensing, 43(9), 2148-2159. https://doi.org/10.1109/TGRS.2005.852480 Topographic correction consists of the following steps: 1. calculate incidence angle if it is not provided 2. estimate C-Correction value 3. apply C-Correction value to the image data TODO: Rationale/ examples for using different fitting algorithms """ import numpy as np from .c import calc_c, get_band_samples, get_cosine_i_samples import ray from ..misc import update_topo from ..misc import progbar def calc_scsc_c1(solar_zn,slope): """ Calculate c1 All input geometry units must be in radians. Args: solar_zn (numpy.ndarray): Solar zenith angle. slope (numpy.ndarray): Ground slope. Returns: numpy.ndarray: C1. """ # Eq 11. Soenen et al. 2005 scsc_c1 = np.cos(solar_zn) * np.cos(slope) return scsc_c1 def calc_scsc_coeffs(hy_obj,topo_dict): ''' Args: hy_obj (TYPE): DESCRIPTION. Returns: None. ''' topo_dict['coeffs'] = {} cosine_i = hy_obj.cosine_i() for band_num,band in enumerate(hy_obj.bad_bands): if ~band: band = hy_obj.get_band(band_num,mask='calc_topo') topo_dict['coeffs'][band_num] = calc_c(band,cosine_i[hy_obj.mask['calc_topo']], fit_type=topo_dict['c_fit_type']) hy_obj.topo = topo_dict def calc_scsc_coeffs_group(actors,topo_dict,group_tag): cosine_i_samples = ray.get([a.do.remote(get_cosine_i_samples) for a in actors]) cosine_i_samples = np.concatenate(cosine_i_samples) print(f'Topo Subgroup {group_tag}') bad_bands = ray.get(actors[0].do.remote(lambda x: x.bad_bands)) coeffs = {} for band_num,band in enumerate(bad_bands): if ~band: coeffs[band_num] = {} band_samples = ray.get([a.do.remote(get_band_samples, {'band_num':band_num}) for a in actors]) band_samples = np.concatenate(band_samples) coeffs[band_num] = calc_c(band_samples,cosine_i_samples,fit_type=topo_dict['c_fit_type']) progbar(np.sum(~bad_bands[:band_num+1]),np.sum(~bad_bands)) print('\n') #Update TOPO coeffs _ = ray.get([a.do.remote(update_topo,{'key':'coeffs', 'value': coeffs}) for a in actors]) _ = ray.get([a.do.remote(update_topo,{'key':'subgroup', 'value': group_tag}) for a in actors]) def apply_scsc_band(hy_obj,band,index): ''' Args: hy_obj (TYPE): DESCRIPTION. band (TYPE): DESCRIPTION. index (TYPE): DESCRIPTION. Returns: band (TYPE): DESCRIPTION. ''' c1 = np.cos(hy_obj.get_anc('slope')) * np.cos(hy_obj.get_anc('solar_zn')) cosine_i = hy_obj.cosine_i() C = hy_obj.topo['coeffs'][index] correction_factor = (c1 + C)/(cosine_i + C) band[hy_obj.mask['calc_topo']] = band[hy_obj.mask['calc_topo']] * correction_factor[hy_obj.mask['calc_topo']] band[~hy_obj.mask['no_data']] = hy_obj.no_data return band def apply_scsc(hy_obj,data,dimension,index): ''' Apply SCSS correction to a slice of the data Args: hy_obj (TYPE): DESCRIPTION. band (TYPE): DESCRIPTION. index (TYPE): DESCRIPTION. Returns: band (TYPE): DESCRIPTION. ''' if 'c1' not in hy_obj.ancillary.keys(): c1 = np.cos(hy_obj.get_anc('slope')) * np.cos(hy_obj.get_anc('solar_zn')) hy_obj.ancillary['c1'] = c1 if 'cosine_i' not in hy_obj.ancillary.keys(): cosine_i = hy_obj.cosine_i() hy_obj.ancillary['cosine_i'] = cosine_i C_bands = list([int(x) for x in hy_obj.topo['coeffs'].keys()]) C = np.array(list(hy_obj.topo['coeffs'].values())) #Convert to float data = data.astype(np.float32) hy_obj.topo['coeffs'] = {int(k): hy_obj.topo['coeffs'][k] for k in hy_obj.topo['coeffs']} if (dimension != 'band') & (dimension != 'chunk'): if dimension == 'line': #index= 3000 #data = hy_obj.get_line(3000) mask = hy_obj.mask['apply_topo'][index,:] cosine_i = hy_obj.ancillary['cosine_i'][[index],:].T c1 = hy_obj.ancillary['c1'][[index],:].T elif dimension == 'column': #index= 300 #data = hy_obj.get_column(index) mask = hy_obj.mask['apply_topo'][:,index] cosine_i = hy_obj.ancillary['cosine_i'][:,[index]] c1 = hy_obj.ancillary['c1'][:,[index]] elif dimension == 'pixels': #index = [[2000,2001],[200,501]] y,x = index #data = hy_obj.get_pixels(y,x) mask = hy_obj.mask['apply_topo'][y,x] cosine_i = hy_obj.ancillary['cosine_i'][[y],[x]].T c1 = hy_obj.ancillary['c1'][[y],[x]].T correction_factor = np.ones(data.shape) correction_factor[:,C_bands] = (c1 + C)/(cosine_i + C) data[mask,:] = data[mask,:]*correction_factor[mask,:] elif dimension == 'chunk': #index = 200,501,3000,3501 x1,x2,y1,y2 = index #data = hy_obj.get_chunk(x1,x2,y1,y2) mask = hy_obj.mask['apply_topo'][y1:y2,x1:x2] cosine_i = hy_obj.ancillary['cosine_i'][y1:y2,x1:x2][:,:,np.newaxis] c1 = hy_obj.ancillary['c1'][y1:y2,x1:x2][:,:,np.newaxis] correction_factor = np.ones(data.shape) correction_factor[:,:,C_bands] = (c1 + C)/(cosine_i + C) data[mask,:] = data[mask,:]*correction_factor[mask,:] elif (dimension == 'band') and (index in hy_obj.topo['coeffs']): #index= 8 #data = hy_obj.get_band(index) C = hy_obj.topo['coeffs'][index] correction_factor = (hy_obj.ancillary['c1'] + C)/(hy_obj.ancillary['cosine_i'] + C) data[hy_obj.mask['apply_topo']] = data[hy_obj.mask['apply_topo']] * correction_factor[hy_obj.mask['apply_topo']] return data