307 lines
11 KiB
Python
307 lines
11 KiB
Python
import numpy as np
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import os
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try:
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from osgeo import gdal
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GDAL_AVAILABLE = True
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except ImportError:
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GDAL_AVAILABLE = False
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class Hedley:
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def __init__(self, img_path, shp_path=None, NIR_band=47, water_mask=None,
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output_path=None, block_size=1000):
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"""
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Hedley 耀斑去除算法 - 分块逐波段处理版本
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:param img_path (str): 输入影像文件路径（GDAL可读取的格式）
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:param shp_path (str, optional): 深水区域shapefile，已废弃，请使用water_mask
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:param NIR_band (int): NIR波段索引（默认47，对应842.36nm）
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:param water_mask (np.ndarray or str or None): 水域掩膜
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:param output_path (str): 输出文件路径（必须提供，用于分块写入）
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:param block_size (int): 分块大小（默认1000）
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"""
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if not GDAL_AVAILABLE:
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raise ImportError("GDAL未安装，无法读取影像文件")
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self.img_path = img_path
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self.NIR_band = int(float(NIR_band))
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self.water_mask = None
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self.water_mask_path = water_mask
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self.output_path = output_path
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self.block_size = block_size
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self.R_min = None
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self.corr_list = None # 全局协方差系数列表
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# 打开影像
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self.dataset = gdal.Open(img_path, gdal.GA_ReadOnly)
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if self.dataset is None:
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raise ValueError(f"无法打开影像文件: {img_path}")
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self.width = self.dataset.RasterXSize
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self.height = self.dataset.RasterYSize
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self.n_bands = self.dataset.RasterCount
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def _load_water_mask(self):
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"""延迟加载水域掩膜"""
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if self.water_mask_path is None:
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return None
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if isinstance(self.water_mask_path, np.ndarray):
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if self.water_mask_path.shape[:2] != (self.height, self.width):
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raise ValueError(
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f"掩膜尺寸 {self.water_mask_path.shape[:2]} 与图像尺寸 {(self.height, self.width)} 不匹配"
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)
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return (self.water_mask_path > 0).astype(np.uint8)
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if isinstance(self.water_mask_path, str):
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if self.water_mask_path.lower().endswith('.shp'):
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raise ValueError("请先栅格化shapefile为栅格掩膜文件")
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mask_dataset = gdal.Open(self.water_mask_path, gdal.GA_ReadOnly)
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if mask_dataset is None:
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raise ValueError(f"无法打开掩膜文件: {self.water_mask_path}")
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mask_array = mask_dataset.GetRasterBand(1).ReadAsArray()
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mask_dataset = None
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if mask_array.shape != (self.height, self.width):
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raise ValueError(
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f"掩膜尺寸 {mask_array.shape} 与图像尺寸 {(self.height, self.width)} 不匹配"
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)
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return (mask_array > 0).astype(np.uint8)
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return None
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def covariance_NIR(self, NIR, b):
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"""计算 NIR 与波段 b 之间的协方差系数 b_i = Cov(NIR,b) / Var(NIR)"""
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n = len(NIR)
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nir_mean = np.mean(NIR)
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b_mean = np.mean(b)
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pij = np.mean((NIR - nir_mean) * (b - b_mean))
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pjj = np.mean((NIR - nir_mean) ** 2)
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return pij / pjj if pjj != 0 else 0.0
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def _scan_global_stats(self, sample_step=20):
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"""
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扫描全图获取全局 R_min
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使用重采样方式扫描，大幅降低内存占用。
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"""
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print(f"[Hedley] 扫描全局统计量（采样步长={sample_step}）...")
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water_mask = self._load_water_mask()
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nir_samples = []
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sample_count = 0
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for y_off in range(0, self.height, self.block_size):
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y_end = min(y_off + self.block_size, self.height)
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block_height = y_end - y_off
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nir_band = self.dataset.GetRasterBand(self.NIR_band + 1)
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nir_block = nir_band.ReadAsArray(0, y_off, self.width, block_height)
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nir_band = None
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if water_mask is not None:
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mask_block = water_mask[y_off:y_end, :]
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mask_bool = mask_block.astype(bool)
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else:
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mask_bool = np.ones((block_height, self.width), dtype=bool)
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if mask_bool.any():
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nir_sampled = nir_block[mask_bool][::sample_step]
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nir_samples.append(nir_sampled)
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sample_count += nir_sampled.size
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del nir_block, mask_block
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if sample_count == 0:
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self.R_min = 0.0
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else:
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all_nir = np.concatenate(nir_samples)
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self.R_min = float(np.percentile(all_nir, 5, method='nearest'))
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del all_nir
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print(f"[Hedley] 全局 R_min={self.R_min:.4f}")
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def _compute_corr_list(self, sample_step=5):
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"""
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计算每个波段与NIR的协方差系数 corr_list[b] = Cov(NIR, band_b) / Var(NIR)
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全分辨率扫描，逐波段读取，每波段内存 ≈ block_size²
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由于需要相关性计算，需要足够多的样本，取sample_step=5
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"""
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print(f"[Hedley] 计算全局协方差系数列表（采样步长={sample_step}）...")
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water_mask = self._load_water_mask()
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# 预收集NIR和每个波段的样本数据
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nir_samples = []
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band_samples = [[] for _ in range(self.n_bands)]
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for y_off in range(0, self.height, self.block_size):
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y_end = min(y_off + self.block_size, self.height)
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block_height = y_end - y_off
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# 读取NIR波段（每块只读一次）
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nir_band = self.dataset.GetRasterBand(self.NIR_band + 1)
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nir_block = nir_band.ReadAsArray(0, y_off, self.width, block_height).astype(np.float32)
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nir_band = None
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# 取 NIR 样本（每块只取一次，放在波段循环外）
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if water_mask is not None:
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mask_block = water_mask[y_off:y_end, :]
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mask_bool = mask_block.astype(bool)
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else:
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mask_bool = np.ones((block_height, self.width), dtype=bool)
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if mask_bool.any():
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nir_sampled = nir_block[mask_bool][::sample_step]
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nir_samples.append(nir_sampled)
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# 逐波段读取并采样（all_band 严格使用单波段切片）
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for b in range(self.n_bands):
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band = self.dataset.GetRasterBand(b + 1)
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block = band.ReadAsArray(0, y_off, self.width, block_height).astype(np.float32)
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band = None
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if mask_bool.any():
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band_sampled = block[mask_bool][::sample_step]
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band_samples[b].append(band_sampled)
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del block
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del nir_block
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# 汇总并计算相关系数
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if len(nir_samples) == 0 or sum(len(s) for s in nir_samples) == 0:
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self.corr_list = [0.0] * self.n_bands
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else:
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all_nir = np.concatenate(nir_samples)
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self.corr_list = []
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for b in range(self.n_bands):
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all_band = np.concatenate(band_samples[b])
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corr = self.covariance_NIR(all_nir, all_band)
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self.corr_list.append(float(corr))
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del all_nir
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for b in range(self.n_bands):
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band_samples[b] = None
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print(f"[Hedley] 协方差系数: min={min(self.corr_list):.4f}, max={max(self.corr_list):.4f}")
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def _process_block(self, x_off, y_off, x_size, y_size):
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"""
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处理单个分块
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Returns:
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list of np.ndarray: 校正后的波段列表
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"""
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# 读取NIR波段
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nir_band = self.dataset.GetRasterBand(self.NIR_band + 1)
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NIR = nir_band.ReadAsArray(x_off, y_off, x_size, y_size).astype(np.float32)
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nir_band = None
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# 预计算 NIR - R_min
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NIR_diff = NIR - self.R_min
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# 获取掩膜
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water_mask = self._load_water_mask()
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if water_mask is not None:
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y_end = y_off + y_size
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x_end = x_off + x_size
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mask_block = water_mask[y_off:y_end, x_off:x_end].astype(bool)
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else:
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mask_block = None
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# 逐波段处理
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corrected_bands = []
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for b in range(self.n_bands):
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band = self.dataset.GetRasterBand(b + 1)
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R = band.ReadAsArray(x_off, y_off, x_size, y_size).astype(np.float32)
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band = None
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corr = self.corr_list[b]
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# Hedley 校正公式：R_corrected = R - corr * (NIR - R_min)
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corrected = R - corr * NIR_diff
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if mask_block is not None:
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corrected = np.where(mask_block, corrected, R)
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corrected_bands.append(corrected)
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del R
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del NIR, NIR_diff
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return corrected_bands
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def get_corrected_bands(self):
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"""
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执行分块处理，返回校正后的波段列表
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"""
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if self.output_path is None:
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raise ValueError("output_path 必须提供，分块处理需要直接写入文件")
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# Step 1: 扫描全局 R_min
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self._scan_global_stats(sample_step=20)
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# Step 2: 计算协方差系数列表
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self._compute_corr_list(sample_step=5)
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# Step 3: 创建输出文件
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output_dir = os.path.dirname(self.output_path)
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if output_dir and not os.path.exists(output_dir):
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os.makedirs(output_dir, exist_ok=True)
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base_path, ext = os.path.splitext(self.output_path)
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bsq_path = base_path + '.bsq' if ext.lower() != '.bsq' else self.output_path
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geotransform = self.dataset.GetGeoTransform()
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projection = self.dataset.GetProjection()
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driver = gdal.GetDriverByName('ENVI')
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out_dataset = driver.Create(bsq_path, self.width, self.height,
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self.n_bands, gdal.GDT_Float32)
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if out_dataset is None:
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raise ValueError(f"无法创建输出文件: {bsq_path}")
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out_dataset.SetGeoTransform(geotransform)
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out_dataset.SetProjection(projection)
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# Step 4: 分块处理
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n_blocks_x = (self.width + self.block_size - 1) // self.block_size
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n_blocks_y = (self.height + self.block_size - 1) // self.block_size
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total_blocks = n_blocks_x * n_blocks_y
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print(f"[Hedley] 开始分块处理，共 {total_blocks} 块 ({n_blocks_x}×{n_blocks_y})，块大小={self.block_size}")
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block_idx = 0
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for y_off in range(0, self.height, self.block_size):
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y_end = min(y_off + self.block_size, self.height)
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y_size = y_end - y_off
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for x_off in range(0, self.width, self.block_size):
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x_end = min(x_off + self.block_size, self.width)
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x_size = x_end - x_off
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block_idx += 1
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print(f"[Hedley] 处理块 {block_idx}/{total_blocks} (y={y_off}, x={x_off})")
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corrected_bands = self._process_block(x_off, y_off, x_size, y_size)
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for b in range(self.n_bands):
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out_band = out_dataset.GetRasterBand(b + 1)
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out_band.WriteArray(corrected_bands[b], x_off, y_off)
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out_band.FlushCache()
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del corrected_bands
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out_dataset = None
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self.dataset = None
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hdr_path = bsq_path + '.hdr'
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if os.path.exists(hdr_path):
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print(f"[Hedley] 校正完成，已保存至: {bsq_path}")
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else:
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print(f"[Hedley] 校正完成，已保存至: {bsq_path}（警告: 未检测到.hdr文件）")
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return []
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def __del__(self):
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if self.dataset is not None:
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self.dataset = None |