Hyperspectral Detection and Identification with Constrained Target Subspaces

Abstract

Subspace methods for hyperspectral imagery enable detection and identification of targets under unknown environmental conditions by specifying a subspace of possible target spectral signatures (and, optionally, a background subspace) and identifying closely fitting spectra in the image. In this study, detection performance in the thermal infrared (IR) was compared using various constrained and unconstrained basis set expansions of low-dimensional target subspaces. An initial investigation of detection using retrieved atmospheric parameters to reduce subspace size and/or dimensionality has also been performed.