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

Abstract

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

Long-wave infrared signature of aircraft nose and its emissivity for low observability

“…7 passive surveillance, and engagement possible. 9 In this band, the detector can get IR-image of aircraft parts at a relatively low temperature (e.g., aircraft skin), which are hardly seen in SWIR and MWIR bands. 10 Multicolor IR imaging (especially in LWIR) using quantum-well IR photodetectors and HgCdTe, InSb, or GaAs makes most IR-countermeasures ineffective.…”

“…Previous generation IR-guided missiles that operated in short-wave IR (SWIR) band (1.9 to 2.9 μm) and mid-wave IR (MWIR) band (3 to 5 μm) could engage target aircraft from the rear only 8 . Inclusion of LWIR band in IR-seeker on-board missile and its use for IR search/surveillance and track on-board an aircraft has made all aspect detection (including frontal), passive surveillance, and engagement possible 9 . In this band, the detector can get IR-image of aircraft parts at a relatively low temperature (e.g., aircraft skin), which are hardly seen in SWIR and MWIR bands 10 .…”