We have designed and implemented computational neural systems for targ
et enhancement, detection, learning and recognition in visible, multis
pectral infrared (IR), and synthetic aperture radar (SAR) imagery. The
system architectures are motivated by designs of biological vision sy
stems, 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 ta
rgets, and accumulation of evidence across image sequences is also des
cribed. Similar neurocomputational principles and modules are used acr
oss these various sensing domains. We show how 3D target learning and
recognition from visible silhouettes and SAR return patterns are relat
ed. We show how models of contrast enhancement, contour, shading and c
olor vision can be used to enhance targets in multispectral IR and SAR
imagery, aiding in target detection.