{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:40:29Z","timestamp":1760218829365,"version":"build-2065373602"},"reference-count":19,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2014,6,16]],"date-time":"2014-06-16T00:00:00Z","timestamp":1402876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Curvelet transform is a multidirectional multiscale transform that enables sparse representations for signals. Curvelet-based feature extraction for Synthetic Aperture Radar (SAR) naturally enables utilizing spatial locality; the use of curvelet-based feature extraction is a novel method for SAR clustering. The implemented method is based on curvelet subband Gaussian distribution parameter estimation and cascading these estimated values. The implemented method is compared against original data, polarimetric decomposition features and speckle noise reduced data with use of k-means, fuzzy c-means, spatial fuzzy c-means and self-organizing maps clustering methods. Experimental results show that the curvelet subband Gaussian distribution parameter estimation method with use of self-organizing maps has the best results among other feature extraction-clustering performances, with up to 94.94% overall clustering accuracies. The results also suggest that the implemented method is robust against speckle noise.<\/jats:p>","DOI":"10.3390\/rs6065497","type":"journal-article","created":{"date-parts":[[2014,6,16]],"date-time":"2014-06-16T11:52:47Z","timestamp":1402919567000},"page":"5497-5519","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Synthetic Aperture Radar Image Clustering with Curvelet Subband Gauss Distribution Parameters"],"prefix":"10.3390","volume":"6","author":[{"given":"Erkan","family":"Uslu","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Yildiz Technical University, 34220 Istanbul, Turkey"}]},{"given":"Songul","family":"Albayrak","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Yildiz Technical University, 34220 Istanbul, Turkey"}]}],"member":"1968","published-online":{"date-parts":[[2014,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1109\/TGRS.2011.2164085","article-title":"Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty","volume":"50","author":"Yu","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1109\/LGRS.2013.2280025","article-title":"Unsupervised SAR image segmentation based on Triplet Markov fields with graph cuts","volume":"11","author":"Gan","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2126","DOI":"10.1109\/TGRS.2008.918647","article-title":"Spectral clustering ensemble applied to SAR image segmentation","volume":"46","author":"Zhang","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1109\/TGRS.2009.2033588","article-title":"Segmentation of SAR intensity imagery with a Voronoi tessellation, Bayesian inference, and reversible jump MCMC algorithm","volume":"48","author":"Li","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_5","unstructured":"Peng, R., Wang, X., L\u00fc, Y., and Wang, S. 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Xi\u2019an, China.","DOI":"10.1109\/APSAR.2009.5374117"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"716","DOI":"10.3390\/rs5020716","article-title":"Recent trend and advance of synthetic aperture radar with selected topics","volume":"5","author":"Ouchi","year":"2013","journal-title":"Remote Sens"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1109\/TIP.2002.804276","article-title":"Speckle reducing anisotropic diffusion","volume":"11","author":"Yu","year":"2002","journal-title":"IEEE Trans. Image Process"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/MSP.2009.935453","article-title":"The curvelet transform","volume":"27","author":"Ma","year":"2010","journal-title":"IEEE Signal Process. Mag"},{"key":"ref_12","first-page":"861","article-title":"Fast discrete curvelet transforms","volume":"5","author":"Demanet","year":"2005","journal-title":"Multiscale Model. 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[3rd ed].","DOI":"10.1007\/978-3-642-56927-2"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/6\/5497\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:12:28Z","timestamp":1760217148000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/6\/5497"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,6,16]]},"references-count":19,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2014,6]]}},"alternative-id":["rs6065497"],"URL":"https:\/\/doi.org\/10.3390\/rs6065497","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2014,6,16]]}}}