{"title":"Fast InSAR multichannel phase unwrapping for DEM generation","authors":"G. Ferraioli, A. Shabou, F. Tupin, V. Pascazio","doi":"10.1109/URS.2009.5137533","DOIUrl":null,"url":null,"abstract":"In this paper, a method to solve the multichannel phase unwrapping problem is presented. MAP approach together with Markov Random Fields have proved to be effective, allowing to restore the uniqueness of the solution without introducing external constraints to regularize the problem. The idea is to develop a fast algorithm to unwrap the interferometric phase in the multichannel configuration, which is, in the main time, able to provide the global optimum solution. To reach this target, an a priori model based on Total Variation is used together with optimization algorithm based on graph-cut technique. The proposed approach has been tested both on simulated and real data. The obtained results show the effectiveness of our approach.","PeriodicalId":154334,"journal":{"name":"2009 Joint Urban Remote Sensing Event","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Joint Urban Remote Sensing Event","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/URS.2009.5137533","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
In this paper, a method to solve the multichannel phase unwrapping problem is presented. MAP approach together with Markov Random Fields have proved to be effective, allowing to restore the uniqueness of the solution without introducing external constraints to regularize the problem. The idea is to develop a fast algorithm to unwrap the interferometric phase in the multichannel configuration, which is, in the main time, able to provide the global optimum solution. To reach this target, an a priori model based on Total Variation is used together with optimization algorithm based on graph-cut technique. The proposed approach has been tested both on simulated and real data. The obtained results show the effectiveness of our approach.