Evaluation of different structural models for target detection in hyperspectral imagery

Abstract

Target detection is an essential component for defense, security and medical applications of hyperspectral imagery. Structured and unstructured models are used to model variability of spectral signatures, for the design of information extraction algorithms. In structured models, spectral variability is modeled using different geometric representations. In linear approaches, the spectral signatures are assumed to be generated by the linear combination of basis vectors. The nature of the basis vectors, and its allowable linear combinations, define different structural models such as vector subspaces, polyhedral cones, and convex hulls. In this paper, we investigate the use of these models to describe background of hyperspectral images, and study the performance of target detection algorithms based on these models. We also study the effect of the model order in the performance of target detection algorithms based on these models. Results show that model order is critical to algorithm performance. Underfitting or overfitting result in poor performance. Models based on subspace are of lower order than those based on polyhedral cones or convex hulls. With good target to background contrast all models perform well.