Improved Algorithm of Decorrelated Target Detection for Polarimetric SAR Images

Abstract

For the characterisitcs of multi-channel data of the polarimetric Synthetic Aperture Radar(SAR) image,an improved algorithm of polarimetric SAR image decorrelation target detection is proposed.The classical Polarimetric Matching Filter (PMF) metric is anaylzed in solving the optimal weight value and the subsequent derivation of the statistical distribution.The real polarimetric SAR data is used to validate the unignored correlations between the channels of the PMF metric for different surface features.The channels of the PMF metric are decorrelated according to the decorrelation theory for two-dimensional Gaussian distribution,thereby obtaining a new Decorrelated Polarimetric Matched Filter(DPMF) metric.The solution for the optimal weight of the DPMF metric is suitable for more general case compared with PMF metric.There is no correlation betwwen the channels of the DPMF metric after decorrelation and meets the independent distribution of complex Gauss random variables which makes the subsequent derivation of the statistical distribution more stringent.Experimental results for the real polarimetric SAR data show that the detection algorithm based on the DPMF metric can efficiently distinguish target and clutter with a high detection rate and less false alarm rate.