{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:11:38Z","timestamp":1760242298322,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,3,29]],"date-time":"2017-03-29T00:00:00Z","timestamp":1490745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Classification using the rich information provided by time-series and polarimetric Synthetic Aperture Radar (SAR) images has attracted much attention. The key point is to effectively reveal the correlation between different dimensions of information and form a joint feature. In this paper, a multi-dimensional SAR descriptive primitive for each single pixel is firstly constructed, which in the polarimetric scale obtains incoherent information through target decompositions while in the time scale obtains coherent information through stochastic walk. Secondly, for the purpose of feature extraction and dimension reduction, a special feature space mapping for the descriptive primitive of the whole image is proposed based on sparse manifold expression and compressed sensing. Finally, the above feature is inputted into a support vector machine (SVM) classifier. This proposed method can inherently integrate the features of polarimetric SAR times series. Experiment results on three real time-series polarimetric SAR data sets show the effectiveness of our presented approach. The idea of a multi-dimensional descriptive primitive as a convenient tool also opens a new spectrum of potential for further processing of polarimetric SAR image time series.<\/jats:p>","DOI":"10.3390\/ijgi6040097","type":"journal-article","created":{"date-parts":[[2017,3,29]],"date-time":"2017-03-29T11:26:44Z","timestamp":1490786804000},"page":"97","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Sparse Manifold Classification Method Based on a Multi-Dimensional Descriptive Primitive of Polarimetric SAR Image Time Series"],"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":"Signal Processing Laboratory, Electronic Information School, Wuhan University, Wuhan 430072, China"},{"name":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Gong","family":"Han","sequence":"additional","affiliation":[{"name":"Signal Processing Laboratory, Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Di","family":"Feng","sequence":"additional","affiliation":[{"name":"Signal Processing Laboratory, Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Juan","family":"Du","sequence":"additional","affiliation":[{"name":"Remote Sensing and Information Engineering School, Wuhan University, Wuhan 430079, China"}]},{"given":"Mingsheng","family":"Liao","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/34.824819","article-title":"Statistical pattern recognition: A review","volume":"22","author":"Jain","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","unstructured":"Richard, H.J. (1970). Phenomenological theory of radar targets. [Ph.D. Dissertation, Technical University]."},{"key":"ref_3","unstructured":"Chandrasekhar, S. (1950). Radiative Transfer, Clarendon Press."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/36.485127","article-title":"A review of target decomposition theorems in radar polarimetry","volume":"34","author":"Robert","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.isprsjprs.2015.10.003","article-title":"Time series analysis of InSAR data: Methods and trends","volume":"115","author":"Osmanoglu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1109\/LGRS.2013.2293508","article-title":"Multiple-Scale Salient-Region Detection of SAR Image Based on Gamma Distribution and Local Intensity Variation","volume":"11","author":"Zhang","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"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":"Zhang, L., Xia, G.S., Wu, T., Lin, L., and Tai, X.C. (2015). Deep Learning for Remote Sensing Image Understanding. J. Sens., 501."},{"key":"ref_9","unstructured":"Xu, C., Tao, D., and Xu, C. (2013). A Survey on Multi-view Learning. Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nigam, K., and Ghani, R. (2000, January 6\u201311). Analyzing the Effectiveness and Applicability of Co-Training. Proceedings of the Ninth International Conference on Information and Knowledge Management, McLean, VA, USA.","DOI":"10.1145\/354756.354805"},{"key":"ref_11","first-page":"2211","article-title":"Multiple Kernel Learning Algorithms","volume":"12","author":"Gonen","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lu, H., Plataniotis, K.N., and Venetsanopoulos, A. (2013). Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data, Chapman & Hall\/CRC.","DOI":"10.1201\/b16252"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Blum, A., and Mitchell, T. (1998, January 24\u201326). Combining Labeled and Unlabeled Data with Co-Training. Proceedings of the Workshop on Computational Learning Theory, COLT, Madison, WI, USA.","DOI":"10.1145\/279943.279962"},{"key":"ref_14","first-page":"2491","article-title":"SimpleMKL","volume":"9","author":"Rakotomamonjy","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_15","unstructured":"Jolliffe, I. (2002). Pincipal Component Analysis, Springer. [2nd ed.]."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Estrada, F.J., Fleet, D.J., and Jepson, A.D. (2009, January 7\u201310). Stochastic Image Denoising. Proceedings of the British Machine Vision Conference, BMVC 2009, London, UK.","DOI":"10.5244\/C.23.117"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, J., Lv, F., Huang, T., and Gong, Y. (2010, January 13\u201318). Locality-Constrained Linear Coding for Image Classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540018"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear Dimensionality Reduction by Locally Linear Embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Luis Vergara and Antonio Soriano and Gonzalo Safont and Addisson Salazar (2016). On the fusion of non-independent detectors. Digit. Signal Process., 50, 24\u201333.","DOI":"10.1016\/j.dsp.2015.11.009"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/4\/97\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:31:32Z","timestamp":1760207492000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/4\/97"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,29]]},"references-count":20,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,4]]}},"alternative-id":["ijgi6040097"],"URL":"https:\/\/doi.org\/10.3390\/ijgi6040097","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2017,3,29]]}}}