{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:11:21Z","timestamp":1760242281773,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,5]],"date-time":"2017-04-05T00:00:00Z","timestamp":1491350400000},"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":["61331016","41371342"],"award-info":[{"award-number":["61331016","41371342"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Basic Research and Development Program of China(973 Program)","award":["2013CB733404"],"award-info":[{"award-number":["2013CB733404"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>This paper presents a classification approach based on attribute learning for high spatial resolution Synthetic Aperture Radar (SAR) images. To explore the representative and discriminative attributes  of  SAR images, first, an iterative unsupervised algorithm is designed to cluster in the low-level feature space, where the maximum edge response and the ratio of mean-to-variance are included; a cross-validation step is applied to prevent overfitting. Second, the most discriminative clustering centers are sorted out to construct an attribute dictionary. By resorting to the attribute dictionary, a representation vector describing certain categories in the SAR image can be generated, which in turn is used to perform the  classifying task. The experiments conducted on TerraSAR-X images indicate that those learned attributes have strong visual semantics, which are characterized by bright and dark spots, stripes, or their combinations. The classification method based on these learned attributes achieves better results.<\/jats:p>","DOI":"10.3390\/ijgi6040111","type":"journal-article","created":{"date-parts":[[2017,4,5]],"date-time":"2017-04-05T10:33:01Z","timestamp":1491388381000},"page":"111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Attribute Learning for SAR Image Classification"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4947-662X","authenticated-orcid":false,"given":"Chu","family":"He","sequence":"first","affiliation":[{"name":"Electronic and Information School, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinlong","family":"Liu","sequence":"additional","affiliation":[{"name":"Electronic and Information School, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenyao","family":"Kang","sequence":"additional","affiliation":[{"name":"Electronic and Information School, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Chen","sequence":"additional","affiliation":[{"name":"Electronic and Information School, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingsheng","family":"Liao","sequence":"additional","affiliation":[{"name":"Electronic and Information School, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1109\/JPROC.2012.2220511","article-title":"Very-high-resolution airborne synthetic aperture radar imaging: Signal processing and applications","volume":"101","author":"Reigber","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1109\/TGRS.2004.834630","article-title":"A new statistical model for Markovian classification of urban areas in high-resolution SAR images","volume":"42","author":"Tison","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1109\/TPAMI.2011.274","article-title":"SAR image segmentation based on level set approach and \n        \n          \n            \n              G\n              A\n              0\n            \n          \n        \n       model","volume":"34","author":"Marques","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_6","unstructured":"Torres-Torriti, M., and Jouan, A. (2001, January 7\u201310). Gabor vs. GMRF features for SAR imagery classification. Proceedings of the 2001 International Conference on Image Processing (ICIP), Thessaloniki, Greece."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4576","DOI":"10.1109\/TGRS.2012.2236338","article-title":"Texture classification of PolSAR data based on sparse coding of wavelet polarization textons","volume":"51","author":"He","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","unstructured":"Li, S.Z. (2009). Markov Random Field Modeling in Image Analysis, Springer Science & Business Media."},{"key":"ref_9","unstructured":"Lafferty, J.D., Mccallum, A., and Pereira, F.C.N. (July, January 28). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings of the Eighteenth International Conference on Machine Learning, Williams College, MA, USA."},{"key":"ref_10","unstructured":"Koller, D., and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques, MIT Press."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5631","DOI":"10.1109\/TGRS.2016.2561842","article-title":"Extinction profiles for the classification of remote sensing data","volume":"54","author":"Ghamisi","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5952","DOI":"10.1109\/TGRS.2016.2576978","article-title":"Object-based morphological profiles for classification of remote sensing imagery","volume":"54","author":"Klotz","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1109\/TGRS.2015.2496167","article-title":"Histogram-based attribute profiles for classification of very high resolution remote sensing images","volume":"54","author":"Demir","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1109\/LGRS.2010.2100363","article-title":"Bag-of-visual-words based on clonal selection algorithm for SAR image classification","volume":"8","author":"Feng","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5158","DOI":"10.1109\/JSTARS.2015.2495267","article-title":"Remote sensing image classification: No features, no clustering","volume":"8","author":"Cui","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1109\/JSTARS.2013.2293343","article-title":"Nonlinear compressed sensing-based LDA topic model for polarimetric SAR image classification","volume":"7","author":"He","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Parikh, D., and Grauman, K. (2011, January 6\u201313). Relative attributes. Proceedings of the 2011 IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126281"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Scheirer, W.J., Kumar, N., Belhumeur, P.N., and Boult, T.E. (2012, January 16\u201321). Multi-attribute spaces: Calibration for attribute fusion and similarity search. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248021"},{"key":"ref_19","unstructured":"Felix, X.Y., Ji, R., Tsai, M.H., Ye, G., and Chang, S.F. (2012, January 16\u201321). Weak attributes for large-scale image retrieval. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA."},{"key":"ref_20","unstructured":"Khan, F.S., Anwer, R.M., Van De Weijer, J., Bagdanov, A.D., Vanrell, M., and Lopez, A.M. (2012, January 16\u201321). Color attributes for object detection. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lampert, C.H., Nickisch, H., and Harmeling, S. (2009, January 20\u201325). Learning to detect unseen object classes by between-class attribute transfer. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206594"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Singh, S., Gupta, A., and Efros, A.A. (2012). Unsupervised discovery of mid-level discriminative patches. Computer Vision\u2013ECCV 2012, Springer.","DOI":"10.1007\/978-3-642-33709-3_6"},{"key":"ref_23","unstructured":"Fukuda, S., and Hirosawa, H. (2001, January 9\u201313). Support vector machine classification of land cover: Application to polarimetric SAR data. Proceedings of the IEEE 2001 International Geoscience and Remote Sensing Symposium, Sydney, Ausralia."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1109\/36.7708","article-title":"A statistical and geometrical edge detector for SAR images","volume":"26","author":"Touzi","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1109\/LGRS.2012.2235406","article-title":"Ratio-detector-based feature extraction for very high resolution SAR image patch indexing","volume":"10","author":"Cui","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TPAMI.2002.1017616","article-title":"An efficient k-means clustering algorithm: Analysis and implementation","volume":"24","author":"Kanungo","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/TGRS.2003.817218","article-title":"Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery","volume":"42","author":"Clausi","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1109\/LGRS.2014.2386351","article-title":"Particle filter sample texton feature for SAR image classification","volume":"12","author":"He","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1177\/1536867X0400400404","article-title":"Confidence intervals for the kappa statistic","volume":"4","author":"Reichenheim","year":"2004","journal-title":"Stata J."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/4\/111\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:32:00Z","timestamp":1760207520000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/4\/111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,5]]},"references-count":29,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,4]]}},"alternative-id":["ijgi6040111"],"URL":"https:\/\/doi.org\/10.3390\/ijgi6040111","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2017,4,5]]}}}