@article {12607,
	title = {Physics-Based Detectors Applied to Long-Wave Infrared Hyperspectral Data},
	year = {2006},
	month = {2006/11//},
	institution = {CENTER FOR AUTOMATION RESEARCH, UNIVERSITY OF MARYLAND COLLEGE PARK},
	abstract = {Long-wave infrared (LWIR) hyperspectral image (HSI) data presents an interesting challenge for automatic target detection algorithms. LWIR HSI data is useful for both day and night operations, but weak signatures like disturbed soil can be problematic for standard matched-filter techniques. In this paper, we augment the standard matched-filter techniques with physics-based information particular to HSI data. Our results show that these physics-based detectors provide improved detection performance with quick processing times.},
	keywords = {*HYPERSPECTRAL IMAGERY, algorithms, AUTOMATIC, DETECTION, FAR INFRARED RADIATION, LOW STRENGTH, Matched filters, MILITARY OPERATIONS, NIGHT, OPTICS, SIGNATURES, SOILS, SPECTROMETERS, TARGET DETECTION},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA481339},
	author = {Broadwater,Joshua and Chellapa, Rama}
}
@article {12681,
	title = {Dimensionality Estimation in Hyperspectral Imagery Using Minimum Description Length},
	year = {2004},
	month = {2004/12//},
	institution = {MARYLAND UNIV COLLEGE PARK CENTER FOR AUTOMATION RESEARCH},
	abstract = {Numerous algorithms have been developed for hyperspectral automatic target recognition (ATR) applications. Many of these algorithms require estimation of a background subspace. The estimation of the background subspace has been addressed using multiple methods. but most of these methods assume a-priori knowledge of the background dimensionality. In order to automate the estimation of the background subspace. we present an algorithm based on minimum description length (MDL) that can identify the background dimension. Results show that the MDL criterion estimates the proper dimension of the background for ATR applications.},
	keywords = {*ALGORITHMS, *HYPERSPECTRAL IMAGERY, *TARGET DETECTION, *TARGET RECOGNITION, AUTOMATIC, BURIED OBJECTS, COMPONENT REPORTS, MINE DETECTION, NUMERICAL MATHEMATICS, SURFACE TARGETS., SYMPOSIA, TARGET DIRECTION, RANGE AND POSITION FINDING},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA431643},
	author = {Broadwater,Joshua B and Meth,Reuven and Chellapa, Rama}
}
