{"title":"Classification using polarimetric and interferometric SAR-data","authors":"M. Hellmann, S. R. Cloude, K. Papathanassiou","doi":"10.1109/IGARSS.1997.606462","DOIUrl":null,"url":null,"abstract":"The investigation presented in this paper demonstrate a first order approach to an automatic classification and extraction of cartographic relevant features from SAR data. The authors propose a fusion of polarimetric and interferometric classification techniques that is able to solve several classification ambiguities which are not resolvable with one method alone and is also able to improve significantly the accuracy of the classification results. The complimentarity of the polarimetric and interferometric coherence based classification approaches and the improvements resulting from their combination are demonstrated using data from the space-shuttle-borne SIR-C/X-SAR radar system.","PeriodicalId":64877,"journal":{"name":"遥感信息","volume":"41 1","pages":"1411-1413 vol.3"},"PeriodicalIF":0.0000,"publicationDate":"1997-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"遥感信息","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.1109/IGARSS.1997.606462","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
The investigation presented in this paper demonstrate a first order approach to an automatic classification and extraction of cartographic relevant features from SAR data. The authors propose a fusion of polarimetric and interferometric classification techniques that is able to solve several classification ambiguities which are not resolvable with one method alone and is also able to improve significantly the accuracy of the classification results. The complimentarity of the polarimetric and interferometric coherence based classification approaches and the improvements resulting from their combination are demonstrated using data from the space-shuttle-borne SIR-C/X-SAR radar system.
期刊介绍:
Remote Sensing Information is a bimonthly academic journal supervised by the Ministry of Natural Resources of the People's Republic of China and sponsored by China Academy of Surveying and Mapping Science. Since its inception in 1986, it has been one of the authoritative journals in the field of remote sensing in China.In 2014, it was recognised as one of the first batch of national academic journals, and was awarded the honours of Core Journals of China Science Citation Database, Chinese Core Journals, and Core Journals of Science and Technology of China. The journal won the Excellence Award (First Prize) of the National Excellent Surveying, Mapping and Geographic Information Journal Award in 2011 and 2017 respectively.
Remote Sensing Information is dedicated to reporting the cutting-edge theoretical and applied results of remote sensing science and technology, promoting academic exchanges at home and abroad, and promoting the application of remote sensing science and technology and industrial development. The journal adheres to the principles of openness, fairness and professionalism, abides by the anonymous review system of peer experts, and has good social credibility. The main columns include Review, Theoretical Research, Innovative Applications, Special Reports, International News, Famous Experts' Forum, Geographic National Condition Monitoring, etc., covering various fields such as surveying and mapping, forestry, agriculture, geology, meteorology, ocean, environment, national defence and so on.
Remote Sensing Information aims to provide a high-level academic exchange platform for experts and scholars in the field of remote sensing at home and abroad, to enhance academic influence, and to play a role in promoting and supporting the protection of natural resources, green technology innovation, and the construction of ecological civilisation.