Please use this identifier to cite or link to this item: https://doi.org/10.1109/TGRS.2002.807001
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dc.titleA novel lacunarity estimation method applied to SAR image segmentation
dc.contributor.authorDu, G.
dc.contributor.authorYeo, T.S.
dc.date.accessioned2014-06-16T09:33:00Z
dc.date.available2014-06-16T09:33:00Z
dc.date.issued2002-12
dc.identifier.citationDu, G., Yeo, T.S. (2002-12). A novel lacunarity estimation method applied to SAR image segmentation. IEEE Transactions on Geoscience and Remote Sensing 40 (12) : 2687-2691. ScholarBank@NUS Repository. https://doi.org/10.1109/TGRS.2002.807001
dc.identifier.issn01962892
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54618
dc.description.abstractBased on the relative differential box-counting algorithm and the gliding-box algorithm, a novel method for estimating the lacunarity features of grayscale digital images is proposed in this paper. Four nature texture images are used to test the performance of the novel lacunarity measure. Comparisons with published methods show that the proposed method can efficiently describe texture images, and provide accurate classification results. Real synthetic aperture radar (SAR) images analyses are found to have different lacunarity values for different regions. We show that good result can be obtained with appropriate lacunarity parameters applied to SAR images segmentation.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TGRS.2002.807001
dc.sourceScopus
dc.subjectImage segmentation
dc.subjectLacunarity
dc.subjectSynthetic aperture radar (SAR)
dc.subjectTexture analysis
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TGRS.2002.807001
dc.description.sourcetitleIEEE Transactions on Geoscience and Remote Sensing
dc.description.volume40
dc.description.issue12
dc.description.page2687-2691
dc.description.codenIGRSD
dc.identifier.isiut000180871300014
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