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# -*- 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 <https://www.gnu.org/licenses/>.
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