Performance evaluation of hyperspectral detection algorithms for subpixel objects

Abstract

One of the fundamental challenges for a hyperspectral imaging surveillance system is the detection of sub-pixel objects in background clutter. The background surrounding the object, which acts as interference, provides the major obstacle to successful detection. Two additional limiting factors are the spectral variabilities of the background and the object to be detected. In this paper, we evaluate the performance of detection algorithms for sub-pixel objects using a replacement signal model, where the spectral variability is modeled by multivariate normal distributions. The detection algorithms considered are the classical matched filter, the matched filter with false alarm mitigation, the mixture tuned matched filter and the finite target matched filter. These algorithms are compared using simulated and actual hyperspectral imaging data.