Hiroyoshi Yamada, R. Komaya, Y. Yamaguchi, R. Sato
{"title":"基于esprit的改进体积散射模型散射功率分解","authors":"Hiroyoshi Yamada, R. Komaya, Y. Yamaguchi, R. Sato","doi":"10.1109/IGARSS.2010.5651248","DOIUrl":null,"url":null,"abstract":"The scattering power decomposition for POLSAR data is one of the powerful tools in the radar polarimetry. There are several model-based decomposition techniques. However, since the number of independent observables in POLSAR images is limited, these techniques require several assumptions to obtain unique solution. The authors have proposed an alternative technique with POL-InSAR dataset. By using the POL-InSAR dataset, we can increase the number of observables. However, selection of volume scattering component was still a problem. Recently, Dr. Arii et. al., proposed a generalized volume scattering model, and applied it to the POLSAR dataset with the adaptive non-negative eigenvalue decomposition technique. In this report, we appy the model to the ESPRIT-based POL-InSAR decomposition technique and verify the estimation performance experimentally.","PeriodicalId":406785,"journal":{"name":"2010 IEEE International Geoscience and Remote Sensing Symposium","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Esprit-based scattering power decomposition by using modified volume scattering model\",\"authors\":\"Hiroyoshi Yamada, R. Komaya, Y. Yamaguchi, R. Sato\",\"doi\":\"10.1109/IGARSS.2010.5651248\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The scattering power decomposition for POLSAR data is one of the powerful tools in the radar polarimetry. There are several model-based decomposition techniques. However, since the number of independent observables in POLSAR images is limited, these techniques require several assumptions to obtain unique solution. The authors have proposed an alternative technique with POL-InSAR dataset. By using the POL-InSAR dataset, we can increase the number of observables. However, selection of volume scattering component was still a problem. Recently, Dr. Arii et. al., proposed a generalized volume scattering model, and applied it to the POLSAR dataset with the adaptive non-negative eigenvalue decomposition technique. In this report, we appy the model to the ESPRIT-based POL-InSAR decomposition technique and verify the estimation performance experimentally.\",\"PeriodicalId\":406785,\"journal\":{\"name\":\"2010 IEEE International Geoscience and Remote Sensing Symposium\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Geoscience and Remote Sensing Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IGARSS.2010.5651248\",\"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 International Geoscience and Remote Sensing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS.2010.5651248","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Esprit-based scattering power decomposition by using modified volume scattering model
The scattering power decomposition for POLSAR data is one of the powerful tools in the radar polarimetry. There are several model-based decomposition techniques. However, since the number of independent observables in POLSAR images is limited, these techniques require several assumptions to obtain unique solution. The authors have proposed an alternative technique with POL-InSAR dataset. By using the POL-InSAR dataset, we can increase the number of observables. However, selection of volume scattering component was still a problem. Recently, Dr. Arii et. al., proposed a generalized volume scattering model, and applied it to the POLSAR dataset with the adaptive non-negative eigenvalue decomposition technique. In this report, we appy the model to the ESPRIT-based POL-InSAR decomposition technique and verify the estimation performance experimentally.