Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle
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...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/834140 |
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| author | Xiangwei Xing Kefeng Ji Huanxin Zou Jixiang Sun |
| author_facet | Xiangwei Xing Kefeng Ji Huanxin Zou Jixiang Sun |
| author_sort | Xiangwei Xing |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-103bea1557ea44eeb8f7922f2a4ac9dc |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-103bea1557ea44eeb8f7922f2a4ac9dc2025-02-03T05:51:26ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/834140834140Sparse Representation Based SAR Vehicle Recognition along with Aspect AngleXiangwei Xing0Kefeng Ji1Huanxin Zou2Jixiang Sun3College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, ChinaCollege of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, ChinaCollege of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, ChinaCollege of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, ChinaAs 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.http://dx.doi.org/10.1155/2014/834140 |
| spellingShingle | Xiangwei Xing Kefeng Ji Huanxin Zou Jixiang Sun Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle The Scientific World Journal |
| title | Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle |
| title_full | Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle |
| title_fullStr | Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle |
| title_full_unstemmed | Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle |
| title_short | Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle |
| title_sort | sparse representation based sar vehicle recognition along with aspect angle |
| url | http://dx.doi.org/10.1155/2014/834140 |
| work_keys_str_mv | AT xiangweixing sparserepresentationbasedsarvehiclerecognitionalongwithaspectangle AT kefengji sparserepresentationbasedsarvehiclerecognitionalongwithaspectangle AT huanxinzou sparserepresentationbasedsarvehiclerecognitionalongwithaspectangle AT jixiangsun sparserepresentationbasedsarvehiclerecognitionalongwithaspectangle |