Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms

Battelle scientists are involved in the development of a real-time multispectral imaging and classification system which can be taken into the field to support automated target/background discrimination. The system also has applications in environmental remote sensing, industrial inspection and medical imaging. The Battelle-developed system consists of a passive, multispectral imaging Electro-Optical (E-O) sensor suite and a real-time digital data collection and data fusion image processor. The E-O 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 device (CCD) cameras and two thermal IR imagers integrated on a common platform. The data collection and processing system consists of video switchers, recorders and a real-time sensor fusion/classification hardware system which takes any three wavebands as input and fuses the bands together by applying look-up tables, derived from tailored neural network algorithms, to classify the scene. The result is then visualized in a video format on a full color, 9-inch, active matrix Liquid Crystal Display (LCD). 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. The optimized classification algorithm is used to populate the look-up tables in the real-time sensor fusion board for use in the field