{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T05:11:28Z","timestamp":1754111488550},"reference-count":11,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2018,6,1]]},"DOI":"10.1587\/transinf.2017edl8281","type":"journal-article","created":{"date-parts":[[2018,5,31]],"date-time":"2018-05-31T22:50:10Z","timestamp":1527807010000},"page":"1703-1706","source":"Crossref","is-referenced-by-count":4,"title":["Extreme Learning Machine with Superpixel-Guided Composite Kernels for SAR Image Classification"],"prefix":"10.1587","volume":"E101.D","author":[{"given":"Dongdong","family":"GUAN","sequence":"first","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoan","family":"TANG","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"WANG","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junda","family":"ZHANG","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] C.J. Oliver, \u201cRain forest classification based on SAR texture,\u201d IEEE Trans. Geosci. Remote Sens., vol.38, no.2, pp.1095-1104, 2000. 10.1109\/36.841988","DOI":"10.1109\/36.841988"},{"key":"2","unstructured":"[2] \u00d6. Aytekin, M. Ko\u00e7, and \u0130. Ulusoy, \u201cLocal primitive pattern for the classification of SAR images,\u201d IEEE Trans. Geosci. Remote Sens., vol.18, no.9, pp.1549-1562, 2012."},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, \u201cExtreme learning machines for regression and multiclass classification,\u201d IEEE Trans. Syst., Man, Cybern.-Part B: Cybernetics, vol.42, no.2, pp.513-529, 2012. 10.1109\/tsmcb.2011.2168604","DOI":"10.1109\/TSMCB.2011.2168604"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mari, J. Vila-Frances, and J. Calpe-Maravilla, \u201cComposite kernels for hyperspectral image Classification,\u201d IEEE Geosci. Remote Sens. Lett., vol.3, no.1, pp.93-97, 2006. 10.1109\/lgrs.2005.857031","DOI":"10.1109\/LGRS.2005.857031"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] Y.C. Zhou, J.T. Peng, and C.L.P. Chen, \u201cExtreme Learning Machine With Composite Kernels for Hyperspectral Image Classification,\u201d IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8, no.6, pp.2351-2360, 2015. 10.1109\/jstars.2014.2359965","DOI":"10.1109\/JSTARS.2014.2359965"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. S\u00fcsstrunk, \u201cSLIC superpixels compared to state-of-the-art superpixel methods,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.34, no.11, pp.2274-2282, 2012. 10.1109\/tpami.2012.120","DOI":"10.1109\/TPAMI.2012.120"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] C.-A. Deledalle, L. Denis, and F. Tupin, \u201cHow to compare noisy patches? Patch similarity beyond Gaussian noise,\u201d International Journal of Computer Vision, vol.99, no.1, pp.86-102, 2012. 10.1007\/s11263-012-0519-6","DOI":"10.1007\/s11263-012-0519-6"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] K. Conradsen, A.A. Nielsen, J. Schou, and H. Skriver, \u201cA test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data,\u201d IEEE Trans. Geosci. Remote Sens., vol.41, no.1, pp.4-19, 2003. 10.1109\/tgrs.2002.808066","DOI":"10.1109\/TGRS.2002.808066"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] C.-C. Chang and C.-J. Lin, \u201cLIBSVM: a library for support vector machines,\u201d ACM Transactions on Intelligent Systems and Technology, vol.2, no.3, pp.1-27, 2011 10.1145\/1961189.1961199","DOI":"10.1145\/1961189.1961199"},{"key":"10","unstructured":"[10] C. Oliver and S. Quegan, Understanding Synthetic Aperture Radar Images, Artech House, Norwood, MA, 1997."},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] J. Gu, L.C. Jiao, S.Y. Yang, F. Liu, B. Hou, Z.Q. Zhao, \u201cA Multi-kernel Joint Sparse Graph for SAR Image Segmentation,\u201d IEEE Journal of Selected Topics In Applied Earth Observations and Remote Sensing, vol.9, no.3, pp.1265-1285, 2016. 10.1109\/jstars.2015.2502991","DOI":"10.1109\/JSTARS.2015.2502991"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E101.D\/6\/E101.D_2017EDL8281\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,6,2]],"date-time":"2018-06-02T04:42:16Z","timestamp":1527914536000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E101.D\/6\/E101.D_2017EDL8281\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,1]]},"references-count":11,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2018]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2017edl8281","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,1]]}}}