Man-Made Target Detection of PolSAR Image Based on Local Convolution Sparse Representation

Abstract

Man-made target detection of PolSAR image is an important part of the application of remote sensing data, and generally, the target occupies very few pixels of the image. Convolutional sparse representation (CSR) is proved able to effectively combine polarimetric and spatial information of a PolSAR image to achieve target detection. However, the acquisition of prior knowledge and the modeling of background are difficult. Moreover, the solution process is highly dependent on the alternating direction method of multipliers (ADMMs) algorithm, which introduces new parameters and has high computational complexity. To solve these problems, this letter proposes a man-made target detection method for PolSAR images based on local CSR (Local-CSR). In this method, a novel target detection framework to combine the polarimetric and spatial information based on CSR is proposed, the target dictionary is constructed by the Local-CSR to avoid the aforementioned problems of ADMM solution procedure and finally realize the man-made target detection of the PolSAR image. Two sets of fully polarimetric SAR data sets are used to demonstrate the performance and the experimental results prove the capacity and validity of the proposed method.