578 lines
21 KiB
Python
578 lines
21 KiB
Python
import gc
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import os
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import numpy as np
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import sys
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import xarray as xr
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import rasterio
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import spectral
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from brdf_model_M02 import M02
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from brdf_model_M02SeaDAS import M02SeaDAS
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from brdf_model_L11 import L11
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from brdf_model_O25 import O25
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from brdf_utils import ADF_OCP, squeeze_trivial_dims
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UNC_LUT_CACHE = {}
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"""
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主 BRDF 校正模块
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输入为 xarray 数据集
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所需的光谱维度为 "bands"，其他维度自由
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输入数据集中必填的字段：
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Rw: 方向性海洋反射率
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sza: 太阳天顶角
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vza: 观测天顶角
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raa: 相对方位角（当太阳和观测在同一侧时，raa=0）
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输入数据集中可选的字段：
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Rw_unc: Rw 的不确定度（若缺失，设为零）
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输出数据集中的字段：
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nrrs: 完全归一化的遥感反射率
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rho_ex_w: nrrs * PI
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omega_b: bb/(a+bb)
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eta_b: bbw/bb
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C_brdf: BRDF 校正因子
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brdf_unc: C_brdf 的不确定度
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nrrs_unc: nrrs 的不确定度
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使用和适配 brdf_hypercp 模块需保留的信息：
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Brdf_hypercp 是 EUMETSAT 研究项目的一部分
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"BRDF correction of S3 OLCI water reflectance products"（S3 OLCI 水体反射率产品的 BRDF 校正），
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合同号：RB_EUM-CO-21-4600002626-JIG。
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研究团队成员：Davide D'Alimonte (davide.dalimonte@aequora.org)，
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Tamito Kajiyama (tamito.kajiyama@aequora.org)，
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Jaime Pitarch (jaime.pitarchportero@artov.ismar.cnr.it)，
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Vittorio Brando (vittorio.brando@cnr.it)，
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Marco Talone (talone@icm.csic.es) 以及
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Constant Mazeran (constant.mazeran@solvo.fr)。
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BRDF 查找表中的相对方位角遵循 OLCI 约定。详见 https://www.eumetsat.int/media/50720，图 6。
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"""
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# ---------------------------------------------------------------------------
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# File I/O helpers
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# ---------------------------------------------------------------------------
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def _resolve_envi_header(filepath):
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"""Return the ENVI header path accepted by spectral."""
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if filepath.lower().endswith(".hdr"):
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return filepath
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hdr_path = filepath + ".hdr"
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if os.path.exists(hdr_path):
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return hdr_path
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base, _ext = os.path.splitext(filepath)
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hdr_path = base + ".hdr"
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if os.path.exists(hdr_path):
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return hdr_path
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return filepath
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def read_bsq(filepath, wavelengths=None, dtype=np.float32):
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"""Read an ENVI BSQ hyperspectral image.
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Parameters
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----------
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filepath : str
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Path to the ENVI header (.hdr) or image file.
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wavelengths : array-like, optional
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Band centre wavelengths in nm. When *None*, wavelengths are read
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from the ENVI header if available.
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Returns
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-------
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data : ndarray, shape (bands, rows, cols), float64
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wavelengths : ndarray, shape (bands,)
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img : spectral image object (metadata / transform access)
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"""
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img = spectral.open_image(_resolve_envi_header(filepath))
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data = np.asarray(img.open_memmap(interleave="source"), dtype=dtype)
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# data = np.transpose(data, (2, 0, 1)) # -> (bands, rows, cols)
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if wavelengths is None:
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if hasattr(img, 'bands') and img.bands.centers is not None:
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wavelengths = np.array(img.bands.centers, dtype=np.float32)
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else:
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n_bands = data.shape[0]
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wavelengths = np.arange(1, n_bands + 1, dtype=np.float32)
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return data, np.asarray(wavelengths, dtype=dtype), img
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def read_bip_angles(filepath, dtype=np.float32):
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"""Read an ENVI BIP angle file and extract the five geometry bands.
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Band layout (1-indexed):
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band 7 -> SZA Solar Zenith Angle
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band 8 -> SAA Solar Azimuth Angle
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band 9 -> VZA Sensor Zenith Angle
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band 10 -> VAA Sensor Azimuth Angle
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band 11 -> RAA Relative Azimuth Angle
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Parameters
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----------
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filepath : str
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Path to the ENVI BIP file (header or image file).
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Returns
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-------
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dict with keys 'sza', 'saa', 'vza', 'vaa', 'raa'.
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Each value is a 2-D ndarray of shape (rows, cols), float64.
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"""
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img = spectral.open_image(_resolve_envi_header(filepath))
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data = np.asarray(img.open_memmap(interleave="source"), dtype=dtype)
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n_bands = data.shape[2]
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if n_bands < 11:
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raise ValueError(
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f"Angle file has only {n_bands} bands; at least 11 are required "
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"(bands 7–11 = SZA, SAA, VZA, VAA, RAA)."
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)
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return {
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'sza': data[:, :, 6].copy(),
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'saa': data[:, :, 7].copy(),
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'vza': data[:, :, 8].copy(),
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'vaa': data[:, :, 9].copy(),
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'raa': data[:, :, 10].copy(),
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}
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def read_water_mask(filepath):
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"""Read a single-band GeoTIFF water mask.
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Parameters
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----------
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filepath : str
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Path to the GeoTIFF file.
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Returns
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-------
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mask : ndarray, shape (rows, cols), int8
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Pixels with value 1 indicate water; all other values are non-water.
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"""
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with rasterio.open(filepath) as src:
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mask = src.read(1).astype(np.int8)
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return mask
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def save_brdf_result(
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ds_out,
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output_file,
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source_file=None,
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output_var="Rw_brdf",
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output_format="ENVI",
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dtype=np.float32,
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):
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"""Save the BRDF result to ENVI or NetCDF."""
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output_format = output_format.upper()
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if output_var not in ds_out:
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raise KeyError(f"Output variable '{output_var}' not found in result dataset.")
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if output_format == "NETCDF":
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if not output_file.lower().endswith(".nc"):
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output_file = output_file + ".nc"
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ds_out.to_netcdf(output_file)
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return output_file
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if output_format != "ENVI":
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raise ValueError("output_format must be 'ENVI' or 'NETCDF'.")
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cube = np.asarray(ds_out[output_var].values, dtype=dtype)
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cube = np.transpose(cube, (1, 2, 0))
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if output_file.lower().endswith(".hdr"):
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hdr_path = output_file
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else:
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hdr_path = output_file + ".hdr"
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metadata = {
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"interleave": "bsq",
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"description": f"BRDF corrected result: {output_var}",
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"bands": cube.shape[2],
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"lines": cube.shape[0],
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"samples": cube.shape[1],
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}
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if "bands" in ds_out.coords:
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metadata["wavelength"] = [str(v) for v in ds_out.coords["bands"].values.tolist()]
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if source_file is not None:
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metadata["source_file"] = source_file
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spectral.envi.save_image(
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hdr_path,
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cube,
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dtype=dtype,
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interleave="bsq",
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metadata=metadata,
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force=True,
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)
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return hdr_path
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# ---------------------------------------------------------------------------
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# Top-level pipeline
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# ---------------------------------------------------------------------------
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def run_brdf_correction(
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hyperspectral_file,
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angle_file,
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mask_file,
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wavelengths=None,
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brdf_model='L11',
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chunk_size=4096,
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):
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"""BRDF correction pipeline for water pixels in a hyperspectral scene.
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Reads the three input files, identifies water pixels via the mask,
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runs ``brdf_prototype`` only on those pixels, and returns a full-scene
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xarray Dataset where water pixels carry corrected values and non-water
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pixels retain the original Rw (BRDF-derived fields are NaN outside
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the water mask).
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Parameters
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----------
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hyperspectral_file : str
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Path to the ENVI BSQ hyperspectral image (.hdr sidecar required).
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angle_file : str
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Path to the ENVI BIP angle file. Bands 7–11 must be
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SZA, SAA, VZA, VAA, RAA respectively.
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mask_file : str
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Path to the GeoTIFF water mask. Pixels == 1 are treated as water.
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wavelengths : array-like, optional
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Band centre wavelengths in nm. Overrides the header value when given.
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brdf_model : str
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BRDF model identifier: ``'L11'`` (default), ``'M02'``,
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``'M02SeaDAS'``, or ``'O25'``.
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chunk_size : int, optional
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Number of water pixels processed per batch (default 4096).
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Smaller values reduce peak memory at the cost of more loop
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iterations.
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Returns
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-------
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xr.Dataset
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Full-scene dataset with dimensions ``(bands, y, x)``.
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Variables:
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* ``Rw`` – original directional water reflectance
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* ``nrrs`` – normalised remote-sensing reflectance (water only)
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* ``rho_ex_w`` – nrrs × π (water only)
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* ``C_brdf`` – BRDF correction factor (water only)
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* ``brdf_unc`` – uncertainty of C_brdf (water only)
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* ``nrrs_unc`` – uncertainty of nrrs (water only)
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* ``C_brdf_fail`` – flag: True where correction failed (water only)
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* ``water_mask`` – the input mask (1 = water)
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* ``sza``, ``saa``, ``vza``, ``vaa``, ``raa`` – viewing geometry
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"""
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# ------------------------------------------------------------------
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# 1. Read all inputs
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# ------------------------------------------------------------------
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rw_data, wvl, _img_meta = read_bsq(hyperspectral_file, wavelengths, dtype=np.float32)
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angles = read_bip_angles(angle_file, dtype=np.float32)
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water_mask = read_water_mask(mask_file)
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n_bands, n_rows, n_cols = rw_data.shape
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# Sanity-check spatial dimensions
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if angles['sza'].shape != (n_rows, n_cols):
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raise ValueError(
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f"Angle file spatial shape {angles['sza'].shape} does not match "
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f"hyperspectral image shape ({n_rows}, {n_cols})."
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)
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if water_mask.shape != (n_rows, n_cols):
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raise ValueError(
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f"Mask shape {water_mask.shape} does not match "
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f"hyperspectral image shape ({n_rows}, {n_cols})."
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)
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# ------------------------------------------------------------------
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# 2. Identify water pixels
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# ------------------------------------------------------------------
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water_rows, water_cols = np.where(water_mask == 1)
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n_water = water_rows.size
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if n_water == 0:
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raise RuntimeError("No water pixels found in the mask (no pixels == 1).")
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# ------------------------------------------------------------------
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# 3. Pre-allocate full-scene output arrays
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# Water pixels will be filled in chunks; non-water pixels stay NaN.
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# ------------------------------------------------------------------
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shape3 = (n_bands, n_rows, n_cols)
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shape2 = (n_rows, n_cols)
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nrrs_full = np.full(shape3, np.nan, dtype=np.float32)
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rho_ex_w_full = np.full(shape3, np.nan, dtype=np.float32)
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C_brdf_full = np.full(shape3, np.nan, dtype=np.float32)
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brdf_unc_full = np.full(shape3, np.nan, dtype=np.float32)
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nrrs_unc_full = np.full(shape3, np.nan, dtype=np.float32)
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C_brdf_fail_full = np.zeros(shape2, dtype=bool)
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rw_brdf_full = rw_data.copy()
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# ------------------------------------------------------------------
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# 4–6. Chunked BRDF correction
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# Process water pixels in batches of `chunk_size` to bound
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# peak memory. Each batch builds a minimal xarray Dataset,
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# calls brdf_prototype, writes results back to the full-scene
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# arrays, then releases the batch objects.
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# ------------------------------------------------------------------
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n_chunks = int(np.ceil(n_water / chunk_size))
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for chunk_idx in range(n_chunks):
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lo = chunk_idx * chunk_size
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hi = min(lo + chunk_size, n_water)
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rows_c = water_rows[lo:hi]
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cols_c = water_cols[lo:hi]
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# Extract batch vectors
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rw_c = rw_data[:, rows_c, cols_c].T # (chunk, bands)
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sza_c = angles['sza'][rows_c, cols_c] # (chunk,)
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vza_c = angles['vza'][rows_c, cols_c]
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raa_c = angles['raa'][rows_c, cols_c]
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pixel_idx_c = np.arange(hi - lo)
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ds_chunk = xr.Dataset(
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{
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'Rw': xr.DataArray(rw_c, dims=('n', 'bands'),
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coords={'n': pixel_idx_c, 'bands': wvl}),
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'sza': xr.DataArray(sza_c, dims='n',
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coords={'n': pixel_idx_c}),
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'vza': xr.DataArray(vza_c, dims='n',
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coords={'n': pixel_idx_c}),
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'raa': xr.DataArray(raa_c, dims='n',
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coords={'n': pixel_idx_c}),
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}
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)
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ds_corr = brdf_prototype(ds_chunk, brdf_model=brdf_model)
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# Write results back; ds_corr arrays have dims (n, bands) → transpose
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nrrs_full[:, rows_c, cols_c] = ds_corr['nrrs'].values.T
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rho_ex_w_full[:, rows_c, cols_c] = ds_corr['rho_ex_w'].values.T
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C_brdf_full[:, rows_c, cols_c] = ds_corr['C_brdf'].values.T
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brdf_unc_full[:, rows_c, cols_c] = ds_corr['brdf_unc'].values.T
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nrrs_unc_full[:, rows_c, cols_c] = ds_corr['nrrs_unc'].values.T
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C_brdf_fail_full[rows_c, cols_c] = ds_corr['C_brdf_fail'].values
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rw_brdf_full[:, rows_c, cols_c] = ds_corr['rho_ex_w'].values.T
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# Release batch objects so GC can reclaim memory
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del rw_c, sza_c, vza_c, raa_c, pixel_idx_c, ds_chunk, ds_corr
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gc.collect()
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# ------------------------------------------------------------------
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# 7. Assemble full-scene output Dataset
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# ------------------------------------------------------------------
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full_coords = {'bands': wvl, 'y': np.arange(n_rows), 'x': np.arange(n_cols)}
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dims3 = ('bands', 'y', 'x')
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dims2 = ('y', 'x')
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ds_out = xr.Dataset(
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{
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'Rw': xr.DataArray(rw_data, dims=dims3, coords=full_coords),
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'Rw_brdf': xr.DataArray(rw_brdf_full, dims=dims3, coords=full_coords),
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'nrrs': xr.DataArray(nrrs_full, dims=dims3, coords=full_coords),
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'rho_ex_w': xr.DataArray(rho_ex_w_full, dims=dims3, coords=full_coords),
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'C_brdf': xr.DataArray(C_brdf_full, dims=dims3, coords=full_coords),
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'brdf_unc': xr.DataArray(brdf_unc_full, dims=dims3, coords=full_coords),
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'nrrs_unc': xr.DataArray(nrrs_unc_full, dims=dims3, coords=full_coords),
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'C_brdf_fail': xr.DataArray(C_brdf_fail_full, dims=dims2),
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'water_mask': xr.DataArray(water_mask, dims=dims2),
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'sza': xr.DataArray(angles['sza'], dims=dims2),
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'saa': xr.DataArray(angles['saa'], dims=dims2),
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'vza': xr.DataArray(angles['vza'], dims=dims2),
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'vaa': xr.DataArray(angles['vaa'], dims=dims2),
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'raa': xr.DataArray(angles['raa'], dims=dims2),
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}
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)
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return ds_out
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def process_brdf_files(
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hyperspectral_file,
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angle_file,
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mask_file,
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output_file,
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wavelengths=None,
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brdf_model="L11",
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output_var="Rw_brdf",
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output_format="ENVI",
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chunk_size=4096,
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):
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"""Run BRDF correction from three input files and save the result."""
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ds_out = run_brdf_correction(
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hyperspectral_file=hyperspectral_file,
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angle_file=angle_file,
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mask_file=mask_file,
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wavelengths=wavelengths,
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brdf_model=brdf_model,
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chunk_size=chunk_size,
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)
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saved_path = save_brdf_result(
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ds_out=ds_out,
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output_file=output_file,
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source_file=hyperspectral_file,
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output_var=output_var,
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output_format=output_format,
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)
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return ds_out, saved_path
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# ---------------------------------------------------------------------------
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# Core BRDF correction (unchanged from original)
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# ---------------------------------------------------------------------------
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def brdf_prototype(ds, adf=None, brdf_model='L11'):
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# 测试 BRDF 模型在 GUI 中不支持：强制覆盖
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# brdf_model = 'M02SeaDAS'
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# 压缩单一维度（例如，投放次数、提取次数等）以避免插值问题
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ds, squeezedDims = squeeze_trivial_dims(ds)
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# Initialise model
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if brdf_model == 'M02':
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BRDF_model = M02(bands=ds.bands, aot=ds.aot, wind=ds.wind, adf=None) # Don't use brdf_py.ADF context
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elif brdf_model == 'M02SeaDAS':
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BRDF_model = M02SeaDAS(bands=ds.bands, adf=None) # Don't use brdf_py.ADF context
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elif brdf_model == 'L11':
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BRDF_model = L11(bands=ds.bands, adf=None) # Don't use brdf_py.ADF context
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elif brdf_model == 'O25':
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BRDF_model = O25(bands=ds.bands, adf=None) # Don't use brdf_py.ADF context
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else:
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print("BRDF model %s not supported" % brdf_model)
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sys.exit(1)
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# Init pixel
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BRDF_model.init_pixels(ds['sza'], ds['vza'], ds['raa'])
|
||
|
||
# Compute IOP and normalize by iterating
|
||
ds['nrrs'] = ds['Rw'] / np.pi
|
||
|
||
ds['convergeFlag'] = (0 * ds['sza']).astype(bool)
|
||
ds['C_brdf'] = 0 * ds['nrrs'] + 1
|
||
|
||
for iter_brdf in range(int(BRDF_model.niter)):
|
||
|
||
# M02: Initialise chl_iter
|
||
if brdf_model in ['M02', 'M02SeaDAS'] and (iter_brdf == 0):
|
||
chl_iter = {}
|
||
ds['log10_chl'] = 0 * ds['sza'] + float(np.log10(BRDF_model.OC4MEchl0))
|
||
chl_iter[-1] = 0 * ds['sza'] + float(BRDF_model.OC4MEchl0)
|
||
|
||
ds = BRDF_model.backward(ds, iter_brdf)
|
||
|
||
# M02: Check convergence (dummy for M02SeaDAS for the moment... epsilon set to 0)
|
||
if brdf_model in ['M02', 'M02SeaDAS']:
|
||
chl_iter[iter_brdf] = 10 ** ds['log10_chl']
|
||
# Check if convergence is reached |chl_old-chl_new| < epsilon * chl_new
|
||
ds['convergeFlag'] = (ds['convergeFlag']) | (
|
||
(np.abs(chl_iter[iter_brdf - 1] - chl_iter[iter_brdf]) < float(BRDF_model.OC4MEepsilon) * chl_iter[
|
||
iter_brdf]))
|
||
|
||
# Apply forward model in both geometries
|
||
# forward_mod = BRDF_model.forward(ds).transpose('n', 'bands')
|
||
# forward_mod0 = BRDF_model.forward(ds, normalized=True).transpose('n', 'bands')
|
||
forward_mod = BRDF_model.forward(ds)
|
||
forward_mod0 = BRDF_model.forward(ds, normalized=True)
|
||
|
||
ratio = forward_mod0 / forward_mod
|
||
|
||
# Drop remnant coordinates to avoid ambiguities in the update of the BRDF factor.
|
||
for coord in ratio.coords:
|
||
if coord not in ds['C_brdf'].coords:
|
||
ratio = ratio.drop(coord)
|
||
|
||
# Normalize reflectance
|
||
ds['C_brdf'] = xr.where(ds['convergeFlag'], ds['C_brdf'], ratio)
|
||
ds['nrrs'] = ds['Rw'] / np.pi * ds['C_brdf']
|
||
|
||
# Flag BRDF where NaN and set to 1 (no correction applied).
|
||
ds['C_brdf_fail'] = np.isnan(ds['C_brdf'])
|
||
ds['C_brdf'] = xr.where(ds['C_brdf_fail'], 1, ds['C_brdf'])
|
||
ds['nrrs'] = xr.where(ds['C_brdf_fail'], ds['Rw'] / np.pi, ds['nrrs'])
|
||
|
||
# If QAA_fail is raised, raise C_brdf_fail (but still apply C_brdf).
|
||
if 'QAA_fail' in ds:
|
||
ds['C_brdf_fail'] = (ds['C_brdf_fail']) | (ds['QAA_fail'])
|
||
|
||
# Compute uncertainty
|
||
ds = brdf_uncertainty(ds)
|
||
|
||
# Compute flag
|
||
ds['flags_level2'] = ds['Rw'] * 0 # TODO
|
||
|
||
# Convert to reflectance unit
|
||
ds['rho_ex_w'] = ds['nrrs'] * np.pi
|
||
|
||
# Expand squeezed trivial dimensions
|
||
for dim,d0 in squeezedDims.items():
|
||
ds = ds.expand_dims(dim,axis=d0)
|
||
|
||
return ds
|
||
|
||
def brdf_uncertainty(ds, adf=None):
|
||
''' Compute uncertainty of BRDF factor and propagate to nrrs '''
|
||
|
||
# Read LUT
|
||
if adf is None:
|
||
adf = ADF_OCP
|
||
|
||
unc_lut_path = adf % 'UNC'
|
||
if unc_lut_path not in UNC_LUT_CACHE:
|
||
UNC_LUT_CACHE[unc_lut_path] = xr.open_dataset(unc_lut_path, engine='netcdf4')
|
||
LUT = UNC_LUT_CACHE[unc_lut_path]
|
||
|
||
# Interpolate relative uncertainty
|
||
unc = LUT['unc'].interp(lambda_unc=ds.bands, theta_s_unc=ds.sza, theta_v_unc=ds.vza,
|
||
delta_phi_unc=ds.raa)
|
||
|
||
# Compute absolute uncertainty of factor
|
||
ds['brdf_unc'] = unc * ds['C_brdf']
|
||
|
||
# Flag BRDF_unc where NaN and set to 0
|
||
ds['brdf_unc_fail'] = np.isnan(ds['brdf_unc'])
|
||
ds['brdf_unc'] = xr.where(ds['brdf_unc_fail'], 0, ds['brdf_unc'])
|
||
|
||
# Propagate to nrrs
|
||
Rwex_unc2 = ds['brdf_unc'] * ds['brdf_unc'] * ds['Rw'] * ds['Rw']
|
||
if 'Rw_unc' in ds:
|
||
Rwex_unc2 += ds['C_brdf'] * ds['C_brdf'] * ds['Rw_unc'] * ds['Rw_unc']
|
||
ds['nrrs_unc'] = np.sqrt(Rwex_unc2)/np.pi
|
||
|
||
return ds
|
||
|
||
|
||
def main():
|
||
import argparse
|
||
|
||
parser = argparse.ArgumentParser(description="Water-region BRDF correction for hyperspectral data.")
|
||
parser.add_argument("hyperspectral_file", help="Input ENVI BSQ hyperspectral file or .hdr path.")
|
||
parser.add_argument("angle_file", help="Input ENVI BIP angle file or .hdr path.")
|
||
parser.add_argument("mask_file", help="Input water mask GeoTIFF; pixels == 1 are corrected.")
|
||
parser.add_argument("output_file", help="Output path prefix. ENVI writes .hdr/.img, NetCDF writes .nc.")
|
||
parser.add_argument("--brdf-model", default="L11", choices=["L11", "M02", "M02SeaDAS", "O25"])
|
||
parser.add_argument("--output-var", default="Rw_brdf", choices=["Rw_brdf", "rho_ex_w", "nrrs", "C_brdf"])
|
||
parser.add_argument("--output-format", default="ENVI", choices=["ENVI", "NETCDF"])
|
||
parser.add_argument("--chunk-size", type=int, default=4096,
|
||
help="Number of water pixels per processing batch (default: 4096).")
|
||
args = parser.parse_args()
|
||
|
||
_ds_out, saved_path = process_brdf_files(
|
||
hyperspectral_file=args.hyperspectral_file,
|
||
angle_file=args.angle_file,
|
||
mask_file=args.mask_file,
|
||
output_file=args.output_file,
|
||
brdf_model=args.brdf_model,
|
||
output_var=args.output_var,
|
||
output_format=args.output_format,
|
||
chunk_size=args.chunk_size,
|
||
)
|
||
print(f"BRDF correction finished. Saved to: {saved_path}")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|