{"title":"基于ICA的极地卫星数据ICTD分类研究","authors":"Gabriel Vasile","doi":"10.1109/IGARSS.2019.8900558","DOIUrl":null,"url":null,"abstract":"The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within PolSAR images. This paper addresses an important aspect for applying such methods on real data, namely statistical classification with ICA. A novel algorithm is proposed by adjusting the iterative segmentation from [1], [2] to the particular nature of the Touzi’s polarimetric decomposition [3]. This algorithm is tested using P-band airborne PolSAR data acquired for the ESA campaign TropiSAR campaign.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"8 1","pages":"5129-5132"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"On ICA Based ICTD Classification of Polsar Data\",\"authors\":\"Gabriel Vasile\",\"doi\":\"10.1109/IGARSS.2019.8900558\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within PolSAR images. This paper addresses an important aspect for applying such methods on real data, namely statistical classification with ICA. A novel algorithm is proposed by adjusting the iterative segmentation from [1], [2] to the particular nature of the Touzi’s polarimetric decomposition [3]. This algorithm is tested using P-band airborne PolSAR data acquired for the ESA campaign TropiSAR campaign.\",\"PeriodicalId\":13262,\"journal\":{\"name\":\"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium\",\"volume\":\"8 1\",\"pages\":\"5129-5132\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IGARSS.2019.8900558\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS.2019.8900558","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within PolSAR images. This paper addresses an important aspect for applying such methods on real data, namely statistical classification with ICA. A novel algorithm is proposed by adjusting the iterative segmentation from [1], [2] to the particular nature of the Touzi’s polarimetric decomposition [3]. This algorithm is tested using P-band airborne PolSAR data acquired for the ESA campaign TropiSAR campaign.
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