364 lines
14 KiB
Python
364 lines
14 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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本模块包含应用经验性 BRDF 校正的函数，如下论文所述：
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方程和常数可在以下论文中找到：
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"""
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import numpy as np
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import ray
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from scipy.interpolate import interp1d
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from .kernels import calc_volume_kernel,calc_geom_kernel
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from ..masks import mask_create
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from ..misc import progbar, pairwise
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from ..misc import update_brdf
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from ..plotting import flex_diagno_plot
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def flex_brdf(actors,config_dict):
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brdf_dict= config_dict['brdf']
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if brdf_dict['grouped']:
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calc_flex_group(actors,brdf_dict)
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else:
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_ = ray.get([a.do.remote(calc_flex_single,brdf_dict) for a in actors])
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if "diagnostic_plots" in brdf_dict:
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if brdf_dict['diagnostic_plots']:
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print('Exporting diagnostic plots.')
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_ = ray.get([a.do.remote(flex_diagno_plot,config_dict) for a in actors])
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def ndvi_stratify(hy_obj):
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'''创建 NDVI 分箱分层掩膜
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'''
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ndvi = hy_obj.ndi()
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class_mask = np.zeros((hy_obj.lines, hy_obj.columns))
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for bin_num in hy_obj.brdf['bins']:
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start,end = hy_obj.brdf['bins'][bin_num]
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class_mask[(ndvi > start) & (ndvi <= end)] = bin_num
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class_mask[~hy_obj.mask['calc_brdf']] = 0
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#Subsample data
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idx = np.array(np.where(class_mask!=0)).T
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idxRand= idx[np.random.choice(range(len(idx)),int(len(idx)*(1-hy_obj.brdf['sample_perc'])), replace = False)].T
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class_mask[idxRand[0],idxRand[1]] = 0
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class_mask = class_mask.astype(np.int8)
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hy_obj.ancillary['ndvi_classes'] = class_mask
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def ndvi_2nd_split(ndvi_bins_dynamic, all_ndvi_array, ndvi_bin_range_thres=0.15):
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''' 执行第二次 NDVI 分割
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'''
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ndvi_bin_range_thres = -0.015625 * (len(ndvi_bins_dynamic)-1) + 0.43125
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ndvi_bin_range = np.array(ndvi_bins_dynamic[1:]) - np.array(ndvi_bins_dynamic[:-1])
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bin_for_split = np.argwhere(ndvi_bin_range>=ndvi_bin_range_thres).ravel()
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new_break = []
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if bin_for_split.shape[0]>0:
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for bin_id in bin_for_split:
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# Use median of the bin as the new break point
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new_break += [np.median(all_ndvi_array[(all_ndvi_array > ndvi_bins_dynamic[bin_id]) & (all_ndvi_array < ndvi_bins_dynamic[bin_id+1])]).astype(np.float64)]
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# New list of bin break points
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ndvi_bins_dynamic = sorted(ndvi_bins_dynamic + new_break)
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return ndvi_bins_dynamic
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def ndvi_bins(ndvi,brdf_dict):
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'''计算 NDVI 分箱范围
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'''
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perc_range = brdf_dict['ndvi_perc_max'] - brdf_dict['ndvi_perc_min'] + 1
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ndvi_break_dyn_bin = np.percentile(ndvi[ndvi > 0],
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np.arange(brdf_dict['ndvi_perc_min'],
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brdf_dict['ndvi_perc_max'] + 1,
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perc_range / (brdf_dict['num_bins'] - 1)))
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ndvi_thres = [brdf_dict['ndvi_bin_min']]
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ndvi_thres += ndvi_break_dyn_bin.tolist()
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ndvi_thres += [brdf_dict['ndvi_bin_max']]
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ndvi_thres = sorted(list(set(ndvi_thres)))
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# 对 NDVI 分箱进行第二次分割
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ndvi_thres = ndvi_2nd_split(ndvi_thres, ndvi)
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bins = [[x,y] for x,y in pairwise(ndvi_thres)]
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return bins
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def get_kernel_samples(hy_obj):
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'''计算并采样 BRDF 核函数
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'''
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geom_kernel = hy_obj.geom_kernel(hy_obj.brdf['geometric'],
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b_r=hy_obj.brdf["b/r"] ,
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h_b =hy_obj.brdf["h/b"])
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geom_kernel = geom_kernel[hy_obj.ancillary['ndvi_classes'] !=0]
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vol_kernel = hy_obj.volume_kernel(hy_obj.brdf['volume'])
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vol_kernel = vol_kernel[hy_obj.ancillary['ndvi_classes'] !=0]
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classes = hy_obj.ancillary['ndvi_classes'][hy_obj.ancillary['ndvi_classes'] !=0]
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X = np.vstack([vol_kernel,geom_kernel,
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np.ones(vol_kernel.shape),classes]).T
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return X
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def get_band_samples(hy_obj,args):
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band = hy_obj.get_band(args['band_num'],
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corrections = hy_obj.corrections)
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return band[hy_obj.ancillary['ndvi_classes'] !=0]
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def calc_flex_single(hy_obj,brdf_dict):
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''' 计算单个图像的 BRDF 系数
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'''
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hy_obj.brdf['coeffs'] ={}
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# 确定分箱维度并创建类别掩膜
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if hy_obj.brdf['bin_type'] == 'dynamic':
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bins = ndvi_bins(hy_obj.ndi()[hy_obj.mask['no_data']],brdf_dict)
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# 更新分箱数量
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hy_obj.brdf['num_bins'] = len(bins)
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else:
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bins = brdf_dict['bins']
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hy_obj.brdf['bins'] = {k:v for (k,v) in enumerate(bins,start=1)}
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ndvi_stratify(hy_obj)
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kernel_samples= get_kernel_samples(hy_obj)
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# 计算每个波段和类别的系数
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for band_num,band in enumerate(hy_obj.bad_bands):
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if ~band:
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hy_obj.brdf['coeffs'][band_num] = {}
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band_samples = hy_obj.do(get_band_samples, {'band_num':band_num})
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coeffs= []
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for bin_num in hy_obj.brdf['bins']:
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bin_mask = (kernel_samples[:,3] == bin_num)
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X = kernel_samples[:,:3][bin_mask]
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y = band_samples[bin_mask]
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coeffs.append(np.linalg.lstsq(X, y,rcond=-1)[0].flatten().tolist())
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hy_obj.brdf['coeffs'][band_num] = coeffs
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def calc_flex_group(actors,brdf_dict):
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''' 计算一组图像的 BRDF 系数
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'''
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# 从图像聚合 NDVI 值
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ndvi = ray.get([a.ndi.remote(mask = 'no_data') for a in actors])
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ndvi = np.concatenate([n.flatten() for n in ndvi])
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# 确定分箱维度
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if brdf_dict['bin_type'] == 'dynamic':
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bins = ndvi_bins(ndvi,brdf_dict)
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# 更新分箱数量
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_ = ray.get([a.do.remote(update_brdf,{'key':'num_bins',
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'value': len(bins)}) for a in actors])
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else:
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bins = brdf_dict['bins']
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bins = {k:v for (k,v) in enumerate(bins,start=1)}
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# 更新 BRDF 系数
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_ = ray.get([a.do.remote(update_brdf,{'key':'bins',
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'value': bins}) for a in actors])
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# 创建 NDVI 类别掩膜并采样核函数
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_ = ray.get([a.do.remote(ndvi_stratify) for a in actors])
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kernel_samples = ray.get([a.do.remote(get_kernel_samples) for a in actors])
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kernel_samples = np.concatenate(kernel_samples)
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bad_bands = ray.get(actors[0].do.remote(lambda x: x.bad_bands))
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coeffs = {}
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for band_num,band in enumerate(bad_bands):
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if ~band:
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coeffs[band_num] = {}
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band_samples = ray.get([a.do.remote(get_band_samples,
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{'band_num':band_num}) for a in actors])
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band_samples = np.concatenate(band_samples)
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band_coeffs= []
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for bin_num in bins:
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bin_mask = (kernel_samples[:,3] == bin_num)
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X = kernel_samples[:,:3][bin_mask]
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y = band_samples[bin_mask]
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band_coeffs.append(np.linalg.lstsq(X, y,rcond=-1)[0].flatten().tolist())
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coeffs[band_num] = band_coeffs
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progbar(np.sum(~bad_bands[:band_num+1]),np.sum(~bad_bands))
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print('\n')
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# 更新 BRDF 系数
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_ = ray.get([a.do.remote(update_brdf,{'key':'coeffs',
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'value': coeffs}) for a in actors])
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def apply_flex(hy_obj,data,dimension,index):
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''' 对数据切片应用 flex BRDF 校正
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参数:
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hy_obj : Hytools 类对象。
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data (np.ndarray): 数据切片。
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index (int,list): 数据索引。
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返回:
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data (np.ndarray): BRDF 校正后的数据切片。
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'''
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if 'k_vol' not in hy_obj.ancillary:
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hy_obj.ancillary['k_vol'] = hy_obj.volume_kernel(hy_obj.brdf['volume'])
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if 'k_geom' not in hy_obj.ancillary:
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hy_obj.ancillary['k_geom'] = hy_obj.geom_kernel(hy_obj.brdf['geometric'],
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b_r=hy_obj.brdf["b/r"],
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h_b =hy_obj.brdf["h/b"])
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if ('k_vol_nadir' not in hy_obj.ancillary) or ('k_geom_nadir' not in hy_obj.ancillary):
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solar_zn = hy_obj.brdf['solar_zn_norm_radians'] * np.ones((hy_obj.lines,hy_obj.columns))
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hy_obj.ancillary['k_vol_nadir'] = calc_volume_kernel(0,solar_zn,
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0,0,hy_obj.brdf['volume'])
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hy_obj.ancillary['k_geom_nadir'] = calc_geom_kernel(0,solar_zn,
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0,0,hy_obj.brdf['geometric'],
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b_r=hy_obj.brdf["b/r"],
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h_b =hy_obj.brdf["h/b"])
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if 'apply_brdf' not in hy_obj.mask:
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hy_obj.gen_mask(mask_create,'apply_brdf',hy_obj.brdf['apply_mask'])
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if 'ndvi' not in hy_obj.ancillary:
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hy_obj.ancillary['ndvi'] = hy_obj.ndi()
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if 'interpolators' not in hy_obj.ancillary:
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bin_centers = np.mean(list(hy_obj.brdf['bins'].values()),axis=1)
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hy_obj.ancillary['interpolators'] ={}
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# 生成插值器
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for i in hy_obj.brdf['coeffs']:
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coeffs= np.array(hy_obj.brdf['coeffs'][i])
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interpolator = interp1d(bin_centers, coeffs, kind = hy_obj.brdf['interp_kind'],
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axis=0,fill_value="extrapolate")
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hy_obj.ancillary['interpolators'][int(i)] = interpolator
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# 转换为浮点数
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data = data.astype(np.float32)
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brdf_bands = [int(x) for x in hy_obj.ancillary['interpolators']]
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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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interpolated_f = [hy_obj.ancillary['interpolators'][band](hy_obj.ancillary['ndvi'][index,:]) for band in brdf_bands]
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interpolated_f = np.array(interpolated_f)
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fvol, fgeo, fiso = interpolated_f[:,:,0], interpolated_f[:,:,1], interpolated_f[:,:,2]
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brdf = fvol*hy_obj.ancillary['k_vol'][index,:]
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brdf+= fgeo*hy_obj.ancillary['k_geom'][index,:]
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brdf+= fiso
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brdf_nadir = fvol*hy_obj.ancillary['k_vol_nadir'][index,:]
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brdf_nadir+= fgeo*hy_obj.ancillary['k_geom_nadir'][index,:]
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brdf_nadir+= fiso
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correction_factor = brdf_nadir/brdf
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correction_factor[:,~hy_obj.mask['apply_brdf'][index]] = 1
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data[:,brdf_bands] = data[:,brdf_bands]*correction_factor.T
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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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interpolated_f = [hy_obj.ancillary['interpolators'][band](hy_obj.ancillary['ndvi'][:,index]) for band in brdf_bands]
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interpolated_f = np.array(interpolated_f)
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fvol, fgeo, fiso = interpolated_f[:,:,0], interpolated_f[:,:,1], interpolated_f[:,:,2]
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brdf = fvol*hy_obj.ancillary['k_vol'][:,index]
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brdf+= fgeo*hy_obj.ancillary['k_geom'][:,index]
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brdf+= fiso
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brdf_nadir = fvol*hy_obj.ancillary['k_vol_nadir'][:,index]
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brdf_nadir+= fgeo*hy_obj.ancillary['k_geom_nadir'][:,index]
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brdf_nadir+= fiso
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correction_factor = brdf_nadir/brdf
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correction_factor = np.moveaxis(correction_factor,0,1)
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correction_factor[:,~hy_obj.mask['apply_brdf'][index]] = 1
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data[:,brdf_bands] = data[:,brdf_bands]*correction_factor.T
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elif (dimension == 'band') & (index in brdf_bands):
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# index= 8
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# data = hy_obj.get_band(index)
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interpolated_f = hy_obj.ancillary['interpolators'][index](hy_obj.ancillary['ndvi'])
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fvol, fgeo, fiso = interpolated_f[:,:,0], interpolated_f[:,:,1], interpolated_f[:,:,2]
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brdf = fvol*hy_obj.ancillary['k_vol']
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brdf += fgeo*hy_obj.ancillary['k_geom']
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brdf += fiso
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brdf_nadir = fvol*hy_obj.ancillary['k_vol_nadir']
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brdf_nadir += fgeo*hy_obj.ancillary['k_geom_nadir']
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brdf_nadir += fiso
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correction_factor = brdf_nadir/brdf
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correction_factor[~hy_obj.mask['apply_brdf']] = 1
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data= data* correction_factor
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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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interpolated_f = [hy_obj.ancillary['interpolators'][band](hy_obj.ancillary['ndvi'][y1:y2,x1:x2]) for band in brdf_bands]
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interpolated_f = np.array(interpolated_f)
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interpolated_f = np.swapaxes(interpolated_f,0,-1)
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fvol, fgeo, fiso = interpolated_f[0,:,:,:], interpolated_f[1,:,:,:], interpolated_f[2,:,:,:]
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brdf = fvol*hy_obj.ancillary['k_vol'][y1:y2,x1:x2,np.newaxis]
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brdf+= fgeo*hy_obj.ancillary['k_geom'][y1:y2,x1:x2,np.newaxis]
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brdf+= fiso
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brdf_nadir = fvol*hy_obj.ancillary['k_vol_nadir'][y1:y2,x1:x2,np.newaxis]
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brdf_nadir+= fgeo*hy_obj.ancillary['k_geom_nadir'][y1:y2,x1:x2,np.newaxis]
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brdf_nadir+= fiso
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correction_factor = brdf_nadir/brdf
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correction_factor[~hy_obj.mask['apply_brdf'][y1:y2,x1:x2]] = 1
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data[:,:,brdf_bands] = data[:,:,brdf_bands]*correction_factor
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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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interpolated_f = [hy_obj.ancillary['interpolators'][band](hy_obj.ancillary['ndvi'][y,x]) for band in brdf_bands]
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interpolated_f = np.array(interpolated_f)
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interpolated_f = np.swapaxes(interpolated_f,0,1)
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fvol, fgeo, fiso = interpolated_f[:,:,0], interpolated_f[:,:,1], interpolated_f[:,:,2]
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brdf = fvol*hy_obj.ancillary['k_vol'][y,x,np.newaxis]
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brdf+= fgeo*hy_obj.ancillary['k_geom'][y,x,np.newaxis]
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brdf+= fiso
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brdf_nadir = fvol*hy_obj.ancillary['k_vol_nadir'][y,x,np.newaxis]
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brdf_nadir+= fgeo*hy_obj.ancillary['k_geom_nadir'][y,x,np.newaxis]
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brdf_nadir+= fiso
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correction_factor = brdf_nadir/brdf
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correction_factor[~hy_obj.mask['apply_brdf'][y,x]] = 1
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data[:,brdf_bands] = data[:,brdf_bands]*correction_factor
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return data
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