Surface Reflectance Estimation Using Prior Spatial and Spectral Information

Surface prior-information reflectance estimation (SPIRE) algorithms estimate changes in spectral reflectance using imperfect prior spatial and spectral information. This paper combines spectral and spatial processing to estimate local changes in spectral reflectance between pairs of spectral images under spatially and spectrally varying multiplicative and additive noise, which arise from variations in illumination and atmospheric effects. This approach extends the spatial SPIRE algorithms that were described earlier and utilizes only a prior reflectance image cube and ensembles of typical multiplicative and additive illumination noise spectral vectors that are deduced from images cubes of similar scenes. The method minimizes the impact of environmental noise by replacing with their prior equivalents low-spatial-frequency content and low-order principal components that are known to be noisy based on prior noise spectra. This filtering and substitution process occurs in log space when minimizing the effects of multiplicative noise. Tests on Hyperspectral Digital Imagery Collection Experiment visible near-infrared-shortwave infrared data demonstrated the algorithm's superior ability to estimate absolute reflectance changes under varying illumination conditions. SPIRE performance was nearly identical to the empirical line method (ELM) ground-truth-based atmospheric compensation results and was better than the physics-based Atmospheric removal (ATREM) code overall, particularly, under high clouds and haze. A ldquoSelective SPIRErdquo technique that chooses between combined-spatial/spectral and spectral-only SPIRE reflectance estimates was developed; it maximizes estimation performance on both changed and unchanged pixels. Minimum-distance classification experiments demonstrated Selective SPIRE's superior performance relative to both ATREM and ELM in cross-image supervised classification applications.