Advanced algorithms for autonomous hyperspectral change detection

Abstract

Persistent ISR (intelligence surveillance and reconnaissance) has proven its value as a tactic for national defense. This activity can collect, in particular, information necessary for executing an important concept of operations: wide-area autonomous change detection over long time intervals. Here we describe the remarkable potential of hyperspectral remote sensing systems for enabling such missions, using either visible or thermal infrared wavelengths. First we describe blind change detection, in which no target knowledge is assumed. Targets that have moved can nevertheless be distinguished from naturally occurring background radiometric changes through the use of multivariate statistics informed by simple physics. Detection relies on the ability of hyperspectral algorithms to predict certain conserved properties of background spectral patterns over long time intervals. We also describe a method of mitigating the most worrisome practical engineering difficulty in pixel-level change detection, image misregistration. This has led, in turn, to a method of estimating spectral signature evolution using multiple-scene statistics. Finally, we present a signature-based detection technique that fuses two discrimination mechanisms: use of some prior knowledge of target spectra, and the fact that a change has occurred.