Multicovariance Matched-Filter For Target Detection And Background Recognition

This paper deals with optimum point target detection in a single-frame, multicolor image, such as a multispectral infrared or polarimetric synthetic aperture radar picture. Criteria for optimum filtering here include either maximum output signal-to-noise ratio or a (local, adaptive) Gaussian hypothesis test to distinguish between clutter-alone versus target-plus- clutter. The multicovariance filter completely uses all the joint variability of the problem, in both space and frequency, in a way that generalizes both the traditional spatial matched filter and also techniques involving scalar ratios between frequency bands. This full generalization involves possibly very large matrix blocks, which describe statistical correlations in both space and frequency, not just scalar correlation coefficients between two bands at a time. Some simple conceptual models and examples are discussed which help reduce the complexity of what is potentially a very large linear algebra problem.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.