A Novel Method for LWIR Hyperspectral Target Detection by Means of a Subspace-Based Approach

Abstract

In this work, we present a new approach to detect materials with known spectral emissivity, in data acquired by thermal infrared hyperspectral systems. The method takes into account the spectral variability of the downwelling radiance, commonly neglected in most target detection techniques. We address such variability supposing that the downwelling radiance spans a low-rank subspace, whose basis matrix is learned off-line by means of MODTRAN. We evaluate the performance of the method with simulated data, and present results that show the effectiveness of the proposed algorithm.