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

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.