Neural processing of targets in visible multispectral IR and SAR imagery

We have designed and implemented computational neural systems for target enhancement, detection, learning and recognition in visible, multispectral infrared (IR), and synthetic aperture radar (SAR) imagery. The system architectures are motivated by designs of biological vision systems, drawing insights from retinal processing of contrast and color, occipital lobe processing of shading, color and contour, and temporal lobe processing of pattern and shape. Distinguishing among similar targets, and accumulation of evidence across image sequences is also described. Similar neurocomputational principles and modules are used across these various sensing domains. We show how 3D target learning and recognition from visible silhouettes and SAR return patterns are related. We show how models of contrast enhancement, contour, shading and color vision can be used to enhance targets in multispectral IR and SAR imagery, aiding in target detection.