Adaptive spatial/spectral detection of subpixel targets with unknown spectral characteristics

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Abstract
This paper extends the maximum likelihood concept as applied to the adaptive detection of sub-pixel targets with unknown spectral signatures. The clutter is modeled stochastically with a spatial-spectral covariance matrix. The target model is primarily stochastic and partially deterministic. Within any given spectral band the spatial target signature is deterministic. For the sub-pixel target application, a system point spread function (PSF) is used. The PSF is allowed to vary spectrally, due to the dependency of a sensor's diffractive PSF on the spectral wavelength. The spectral target signature is completely stochastic and must be determined at each pixel using maximum likelihood estimation techniques. Based on these assumptions, an optimal maximum likelihood processor is derived. Encouraging performance results are presented from real IR data. Detection probabilities are shown in many cases to improve significantly when compared to spatial-only detection processes.

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