Target change detection based on Edgeworth statistical distribution features for LF UWB SAR

Abstract

Low frequency ultra-wideband synthetic aperture radar (LF UWB SAR) not only obtains the high-resolution image, but also has the well capability of the foliage penetrating, which is potential of detecting the concealed target under the vegetation. This paper studies the target change detection based on the Edgeworth statistical distribution features in the LF UWB SAR images. First, the Edgeworth expansion is used to estimate the probability density function of the pixel neighborhood, and then the K-L divergence has been used as the standard to evaluate the difference between the probability density functions, to realize the target change detection in the multi-temporal SAR images. Finally, the proposed algorithm is tested based on the LF UWB BSAR data, and then the detection performance is shown and analyzed. The experiment results prove the correctness of the theoretical analysis and the effectiveness of the proposed method.