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AB&1RATNear-simultaneous,multispectral, coregistered imagery of ground target and background signatures were collected over a full diurnal cycle in the MWIR, LWIR, near-infrared, blue, green, and red wavebands using Battelle's portable sensor suite. The imagery data were processed with classical statistical algorithms and artificial neural networks to discriminate target signatures from background clutter and investigate automatic target detection and recognition schemes.

Battelle scientists have assembled a reconfigurable multispectral imaging and classification system which can be taken into the field to support automated real-time targetbackground discrimination. The system may be used for a variety of applications including environmental remote sensing, industrial inspection and medical imaging. This paper discusses hard tactical target and runway detection applications performed with the multispectral system. multispectral imaging electro-optical (EO) sensor suite and a real-time digital data collection and data fusion image processor. The EO sensor suite, able to collect imagery in 12 distinct wavebands from the ultraviolet (UV) through the long wave infrared (LWIR), consists of five charge-coupled deviceThe Battelle-developed system consists of a passive, (CCD) cameras and two thermal IR imagers integrated on a common portable platform. The data collection and processing system consists of video switchers, recorders and a real-time sensor fusiordclassification hardware system which combines any three input wavebands to perform real-time data fusion by applying "look-up tables," derived from tailored neural network algorithm, to classify the imaged scene pixel by pixel. The result is then visualized in a video format on a full color, 9-inch, active matrix Liquid Crystal Display ( E D ) .A variety of classification algorithms including artificial neural networks and data clustering techniques were successfully optimized to perform pixel-level classification of imagery in complex scenes comprised of tactical targets, buildings, roads, aircraft runways, and vegetation. Algorithms implemented included unsupervised maximum likelihood, Linde Buzo Gray, and "fuzzy" clustering algorithms along with Multilayer Perceptron and Learning Vector Quantization (LVQ) neural networks. Supervised clustering of the data was also evaluated. To assess classification robustness, algorithms were tested on imagery recorded over broad periods of time throughout the day. Results were excellent, indicating that scene classification is achievable despite temporal signature variations. Waveband saliency analyses were performed to I3

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