NDLI: A Markov Chain CFAR Detector for Polarimetric Data using Adaptive Linear Discriminant Analysis

Content Provider	IEEE Xplore Digital Library
Author	Fei, C. Liu, T. Lampropoulos, G.A. Sabry, R. Murnaghan, K.
Copyright Year	2008
Description	Author affiliation: A.U.G. Signals Ltd., Toronto, ON (Fei, C.; Liu, T.; Lampropoulos, G.A.) \|\| Natural Resource Canada, Ottawa, ON (Murnaghan, K.) \|\| Defence Res. Dev. Center Ottawa, Ottawa, ON (Sabry, R.)
Abstract	The paper proposes a new Markov chain based CFAR detector for polarimetric data using adaptive linear discriminant analysis. The Markov Chain based CFAR detector extends traditional PDF based CFAR detection to first-order Markov chain model by considering both correlation between neighboring pixels and PDF information in CFAR detection. With the additional correlation information, the proposed approach results in advancing the performance of conventional CFAR detectors. Moreover, to take advantage of full polarizations of polarimetric data, the new polarimetric detector utilizes complementary features from full polarizations for target detection using adaptive Fisher linear discriminant analysis. Our experimental results both show the superiority of the new Markov chain polarimetric CFAR detector over conventional CFAR detectors.
File Size	90274
File Format	PDF
ISBN	9781424428076
DOI	10.1109/IGARSS.2008.4780102
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Linear discriminant analysis Radar detection Polarization Clutter Object detection Pixel Hidden Markov models Statistical distributions Computer vision Fisher linear discriminant analysis Target detection Markov chain
Content Type	Text
Resource Type	Article

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