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"""
Minimal vegetation index extraction (NDVI, ARI, GNDVI only).
"""
import numpy as np
from typing import Dict, Any
import logging
logger = logging.getLogger(__name__)
class VegetationIndexExtractor:
"""Minimal vegetation index extraction."""
def __init__(self, epsilon: float = 1e-10, soil_factor: float = 0.5):
"""Initialize with defaults."""
self.epsilon = epsilon
self.soil_factor = soil_factor
self.index_formulas = {
"NDVI": lambda nir, red: (nir - red) / (nir + red + self.epsilon),
"GNDVI": lambda nir, green: (nir - green) / (nir + green + self.epsilon),
"SAVI": lambda nir, red: ((nir - red) / (nir + red + self.soil_factor)) * (1.0 + self.soil_factor),
}
self.index_bands = {
"NDVI": ["nir", "red"],
"GNDVI": ["nir", "green"],
"SAVI": ["nir", "red"],
}
def compute_vegetation_indices(self, spectral_stack: Dict[str, np.ndarray],
mask: np.ndarray) -> Dict[str, Dict[str, Any]]:
"""Compute NDVI, ARI, and GNDVI."""
indices = {}
for index_name, formula in self.index_formulas.items():
try:
required_bands = self.index_bands[index_name]
if not all(band in spectral_stack for band in required_bands):
continue
band_data = []
for band in required_bands:
arr = spectral_stack[band]
if isinstance(arr, np.ndarray):
arr = arr.squeeze(-1)
band_data.append(np.asarray(arr, dtype=np.float64))
index_values = formula(*band_data).astype(np.float64)
binary_mask = (np.asarray(mask).astype(np.int32) > 0)
masked_values = np.where(binary_mask, index_values, np.nan)
valid_values = masked_values[~np.isnan(masked_values)]
if len(valid_values) > 0:
stats = {
'mean': float(np.mean(valid_values)),
'std': float(np.std(valid_values)),
}
else:
stats = {'mean': 0.0, 'std': 0.0}
indices[index_name] = {
'values': masked_values,
'statistics': stats
}
except Exception as e:
logger.error(f"Failed to compute {index_name}: {e}")
return indices |