A signal processing perspective of hyperspectral imagery analysis techniques

Abstract

A new class of remote sensing data with great potential for the accurate identification of surface materials is termed hyperspectral imagery. Airborne or satellite imaging spectrometers record reflected solar or emissive thermal electromagnetic energy in hundreds of contiguous narrow spectral bands. The substantial dimensionality and unique character of hyperspectral imagery require techniques which differ substantially from traditional imagery analysis. One such approach is offered by a signal processing 'paradigm, which seeks to detect signals in the presence of noise and multiple interfering signals. This study reviews existing hyperspectral imagery analysis techniques from a signal processing perspective and arranges them in a contextual hierarchy. It focuses on a large subset of analysis techniques based on linear transform and subspace projection theory, a well established part of signal processing. Four broad families of linear transformation-based analysis techniques are specified by the amounts of available a priori scene information. Strengths and weaknesses of each technique are developed. In general, the spectral angle mapper (SAM) and the orthogonal subspace projection (OSP) techniques gave the best results and highest signal-to-clutter ratios (SCRs). In the case of minority targets, where a small number of target pixels occurred over the entire scene, the low probability of detection (LPD) technique performed well.