title: Sparse representation based SAR vehicle recognition along with aspect angle.
abstract: As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC) has attracted much attention in synthetic aperture radar (SAR) automatic target recognition (ATR) recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA), in which the correlation between the vehicle's aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA) feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle's aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.
authors.affiliation: College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, China.
authors.forename: Xiangwei
authors.identifier: 0000-0001-6330-590X
authors.initials: X
authors.lastname: Xing
authors.affiliation: College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, China.
authors.forename: Kefeng
authors.initials: K
authors.lastname: Ji
authors.affiliation: College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, China.
authors.forename: Huanxin
authors.initials: H
authors.lastname: Zou
authors.affiliation: College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, China.
authors.forename: Jixiang
authors.initials: J
authors.lastname: Sun
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis