SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation

Abstract

It is necessary to recognize the target in the situation of military battlefield monitoring and civilian real-time monitoring. Sparse representation-based SAR image target recognition method uses training samples or feature information to construct an overcomplete dictionary, which will inevitably affect the recognition speed. In this paper, a method based on monogenic signal and sparse representation is presented for SAR image target recognition. In this method, the extended maximum average correlation height filter is used to train the samples and generate the templates. The monogenic features of the templates are extracted to construct subdictionaries, and the subdictionaries are combined to construct a cascade dictionary. Sparse representation coefficients of the testing samples over the cascade dictionary are calculated by the orthogonal matching tracking algorithm, and recognition is realized according to the energy of the sparse coefficients and voting recognition. The experimental results suggest that the new approach has good results in terms of recognition accuracy and recognition time.

An inertial Douglas–Rachford splitting algorithm for nonconvex and nonsmooth problems

“…To solve this problem, some researchers proposed various algorithms by reducing the computation complexity in wireless communication applications, which can improve the performances of Cell-free massive distributed antenna systems, recognition speeds, online allocation efficiency of virtualized network services with different categories of servers, and so on. [8][9][10] Moreover, abundant achievements in compressed sensing, [11][12][13] sparse approximation of signals 14 and image processing, 15,16 etc. also rely on numerical techniques and algorithms for nonconvex problems.…”