Abstract: The detection and recognition of targets in infrared wide area surveillance systems is made difficult by clutter background and low resolution. Recent advances in technology have made available small and lightweight hyperspectral imaging sensors. Hyperspectral sensors can facilitate the detection of targets in clutter because natural vegetation clutter has a different statistical distribution of radiant energy in the spectral bands than targets. Natural clutter from vegetation can be characterized as a grey body, but man made objects (i.e. targets) are selective radiators. Compared to blackbody radiators, targets emit radiation more strongly at some wavelengths than at others. The approach taken in this paper is to partition the bands into two groups. The targets exhibit substantial color signatures in one group but look like grey bodies in the other group. A generalized formation for combining the hyperspectral bands is derived using maximum likelihood techniques. The algorithm is a generalization of the weighted spectral difference algorithm, and reduces to that form if the image data is preprocessed to make it spatially white. It is also shown that the algorithm is optimum for non- Gaussian noise when the criterion is to minimize the mean square error between the two groups of bands. The algorithm is applied to TIMS multispectral and SMIFTS hyperspectral data to illustrate the algorithm performance.!9
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