6 July 1994 Adaptive spatial/spectral detection of subpixel targets with unknown spectral characteristics
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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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Charles F. Ferrara "Adaptive spatial/spectral detection of subpixel targets with unknown spectral characteristics", Proc. SPIE 2235, Signal and Data Processing of Small Targets 1994, (6 July 1994); doi: 10.1117/12.179107; https://doi.org/10.1117/12.179107

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