Markov Chain CFAR detection for polarimetric data using data fusion

The paper proposes a new Markov chain based CFAR detector for polarimetric data using low level data fusion and high level decision fusion. 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, various data fusion methods are considered to improve detection performance, including polarimetric transformation, principal component analysis, and decision fusion. Our experimental results both show the superiority of the new Markov chain polarimetric CFAR detector over conventional PDF-based CFAR detectors.