Unsupervised Mixture-Eliminating Estimation of Equivalent Number of Looks for PolSAR Data

Abstract

This paper addresses the impact of mixtures between classes on equivalent number of looks (ENL) estimation. We propose an unsupervised ENL estimator for polarimetric synthetic aperture radar (PolSAR) data, which is based on small sample estimates but incorporates a mixture-eliminating (ME) procedure to automatically assess the uniformity of the estimation windows. A statistical feature derived from a combination of linear and logarithmic moments is investigated and adopted in the procedure, as it has different mean values for samples from uniform and nonuniform windows. We introduce an approach to extract the approximated sampling distribution of this test statistic for uniform windows. Then the detection is conducted by a hypothesis test with adaptive thresholds determined by a nonuniformity ratio. Finally the experiments are performed on both simulated and real SAR data. The capability of the unsupervised ME procedure is verified with simulated data. In the real data experiments, the ENL estimates of Flevoland and San Francisco PolSAR images are analyzed, which show the robustness of the proposed ENL estimation for SAR scenes with different complexities.