{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:14:06Z","timestamp":1771949646426,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,2]],"date-time":"2018-03-02T00:00:00Z","timestamp":1519948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Sicence Foundation of China","award":["61671350"],"award-info":[{"award-number":["61671350"]}]},{"name":"National Natural Sicence Foundation of China","award":["61621005"],"award-info":[{"award-number":["61621005"]}]},{"name":"Major Research Plan of the National Natural Science Foundation of China","award":["91438201"],"award-info":[{"award-number":["91438201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Features play an important role in the learning technologies and pattern recognition methods for polarimetric synthetic aperture (PolSAR) image interpretation. In this paper, based on the subspace clustering algorithms, we combine sparse representation, low-rank representation, and manifold graphs to investigate the intrinsic property of PolSAR data. In this algorithm framework, the features are projected through the projection matrix with the sparse or\/and the low rank characteristic in the low dimensional space. Meanwhile, different kinds of manifold graphs explore the geometry structure of PolSAR data to make the projected feature more discriminative. Those learned matrices, that are constrained by the sparsity and low rank terms can search for a few points from the samples and capture the global structure. The proposed algorithms aim at constructing a projection matrix from the subspace clustering algorithms to achieve the features benefiting for the subsequent PolSAR image classification. Experiments test the different combinations of those constraints. It demonstrates that the proposed algorithms outperform other state-of-art linear and nonlinear approaches with better quantization and visualization performance in PolSAR data from spaceborne and airborne platforms.<\/jats:p>","DOI":"10.3390\/rs10030391","type":"journal-article","created":{"date-parts":[[2018,3,2]],"date-time":"2018-03-02T11:53:40Z","timestamp":1519991620000},"page":"391","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Sparse Subspace Clustering-Based Feature Extraction for PolSAR Imagery Classification"],"prefix":"10.3390","volume":"10","author":[{"given":"Bo","family":"Ren","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"},{"name":"Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1996-186X","authenticated-orcid":false,"given":"Biao","family":"Hou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"},{"name":"Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Jin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"},{"name":"Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Licheng","family":"Jiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"},{"name":"Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1049\/el:19900979","article-title":"New decomposition of the radar target scattering matrix","volume":"26","author":"Krogager","year":"1990","journal-title":"Electron. 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