Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 50, Issue 2, Pages 397-408Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2011.2164257
Keywords
Constant false alarm rate (CFAR) detection; data fusion; Markov chain; polarimetric synthetic aperture radar
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Funding
- Canadian Space Agency
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This paper proposes a new Markov-chain-based constant false alarm rate (CFAR) detector for polarimetric data using low-level data fusion and high-level decision fusion. The Markov-chain-based CFAR detector extends traditional probability density function (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, various data fusion methods are considered to improve detection performance, including polarimetric transformation, principal component analysis, and decision fusion. Our experimental results confirm the superiority of the new Markov chain polarimetric CFAR detector over conventional pdf-based CFAR detectors.
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