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7 May 2007 Adaptive constrained signal detector for hyperspectral images
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Abstract
An algorithm called the constrained signal detector (CSD) was recently introduced for the purpose of target detection in hyperspectral images. The CSD assumes that hyperspectral pixels can be modeled as linear mixtures of material signatures and stochastic noise. In theory, the CSD is superior to the popular orthogonal subspace projection (OSP) technique. The CSD requires knowledge of the spectra of the background materials in a hyperspectral image. But in practice the background material spectra are often unknown due to uncertainties in illumination, atmospheric conditions, and the composition of the scene being imaged. In this paper, estimation techniques are used to create an adaptive version of the CSD. This adaptive algorithm uses training data to develop a description of the image background and adaptively implement the CSD. The adaptive CSD only requires knowledge of the target spectrum. It is shown through simulations that the adaptive CSD performs nearly as well as the CSD operating with complete knowledge of the background material spectra. The adaptive CSD is also tested using real hyperspectral image data and its performance is compared to OSP.
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Steven E. Johnson and Michael T. Eismann "Adaptive constrained signal detector for hyperspectral images", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 656505 (7 May 2007); https://doi.org/10.1117/12.720766
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