{"title":"基于独立分量分析的遥感图像无监督分类","authors":"M. C. Sahingil, Y. Ozkazanc","doi":"10.1109/SIU.2010.5650952","DOIUrl":null,"url":null,"abstract":"In this paper, some independent component analysis based unsupervised classification methods for remotely sensed imagery are proposed. In order to determine the validity of the proposed unsupervised classification methodology, some clustering quality metrics are used. According to the obtained results, the successes of proposed methods are compared.","PeriodicalId":152297,"journal":{"name":"2010 IEEE 18th Signal Processing and Communications Applications Conference","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unsupervised classification of remotely sensed images via independent component analysis\",\"authors\":\"M. C. Sahingil, Y. Ozkazanc\",\"doi\":\"10.1109/SIU.2010.5650952\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, some independent component analysis based unsupervised classification methods for remotely sensed imagery are proposed. In order to determine the validity of the proposed unsupervised classification methodology, some clustering quality metrics are used. According to the obtained results, the successes of proposed methods are compared.\",\"PeriodicalId\":152297,\"journal\":{\"name\":\"2010 IEEE 18th Signal Processing and Communications Applications Conference\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE 18th Signal Processing and Communications Applications Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2010.5650952\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE 18th Signal Processing and Communications Applications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2010.5650952","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Unsupervised classification of remotely sensed images via independent component analysis
In this paper, some independent component analysis based unsupervised classification methods for remotely sensed imagery are proposed. In order to determine the validity of the proposed unsupervised classification methodology, some clustering quality metrics are used. According to the obtained results, the successes of proposed methods are compared.
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