Hyperspectral adaptive matched filter detectors: Practical performance comparison

The unified treatment(1) of adaptive matched filter algorithms for target detection in hyperspectral. imaging data included a theoretical analysis of their performance under a Gaussian noise plus interference model. The purpose of this paper is to provide empirical analysis of algorithm performance using HYDICE data sets. First, we provide a concise summary of adaptive matched filter detectors, including their key theoretical assumptions, design parameters, and computational complexity. The widely used generalized likelihood ratio detectors, adaptive subspace detectors, constrained energy minimization (CEM) and orthogonal subspace projection (OSP) algorithm are the focus of the analysis. Second, we investigate how well the signal models used for the development of detection algorithms characterize the HYDICE data. The accurate modeling of the background is crucial for the development of constant false alarm rate (CFAR) detectors. Finally, we compare the different algorithms with regard to two desirable performance properties: capacity to operate in CFAR mode and target "visibility" enhancement.