Dimensionality reduction for spatial-spectral target detection on hyperspectral imagery

Hyperspectral images often contain hundreds of spectral bands. Man-made and natural materials usually exhibit variability in their reflective and emissive response across these bands, which is exploitable via target detection algorithms. The high-dimensional nature of hyperspectral data has driven studies that explored ways to reduce spectral dimensionality without adversely affecting spectral target detection. Recently, spatial-spectral feature extraction techniques have demonstrated additional discrimination capability versus spectral-only approaches in VNIR, SWIR, and LWIR hyperspectral imagery. When spatial descriptors are applied to spectral bands within a hyperspectral image, the length of a resulting spatial-spectral feature vector can be several times that of the original spectrum. While numerous efforts to reduce the dimensionality of hyperspectral imagery have been undertaken, they have not been commonly extended to the spatial-spectral domain. In this work, we address the relatively new problem of spatial-spectral dimensionality reduction through a strategy designed to remove features that neither negatively affect a target detection algorithm's capability to detect targets nor detract from that algorithm's ability to discriminate between targets in an exemplar signature library.