{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T12:49:07Z","timestamp":1766407747093,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,19]],"date-time":"2018-10-19T00:00:00Z","timestamp":1539907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"national natural science foundation of China","doi-asserted-by":"publisher","award":["61701289"],"award-info":[{"award-number":["61701289"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sparse representation (SR) has been verified to be an effective tool for pattern recognition. Considering the multiplicative speckle noise in synthetic aperture radar (SAR) images, a product sparse representation (PSR) algorithm is proposed to achieve SAR target configuration recognition. To extract the essential characteristics of SAR images, the product model is utilized to describe SAR images. The advantages of sparse representation and the product model are combined to realize a more accurate sparse representation of the SAR image. Moreover, in order to weaken the influences of the speckle noise on recognition, the speckle noise of SAR images is modeled by the Gamma distribution, and the sparse vector of the SAR image is obtained from q statistical standpoint. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) database. The experimental results validate the effectiveness and robustness of the proposed algorithm, which can achieve higher recognition rates than some of the state-of-the-art algorithms under different circumstances.<\/jats:p>","DOI":"10.3390\/s18103535","type":"journal-article","created":{"date-parts":[[2018,10,19]],"date-time":"2018-10-19T10:08:02Z","timestamp":1539943682000},"page":"3535","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SAR Target Configuration Recognition via Product Sparse Representation"],"prefix":"10.3390","volume":"18","author":[{"given":"Ming","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi\u2019an 710062, China"},{"name":"School of Computer Science, Shaanxi Normal University, Xi\u2019an 710119, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shichao","family":"Chen","sequence":"additional","affiliation":[{"name":"No. 203 Research Institute of China Ordnance Industries, Xi\u2019an 710065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fugang","family":"Lu","sequence":"additional","affiliation":[{"name":"No. 203 Research Institute of China Ordnance Industries, Xi\u2019an 710065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengdao","family":"Xing","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2206","DOI":"10.1109\/JSTARS.2016.2555938","article-title":"SAR imagery feature extraction using 2DPCA-based two-dimensional neighborhood virtual points discriminant embedding","volume":"9","author":"Pei","year":"2016","journal-title":"IEEE J. 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