@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}
}
