{"title":"Automatic Detection of Surface Deformations by DInSAR","authors":"Karima Hadj-rabah, F. Hocine, A. B. Aissa","doi":"10.1145/3152808.3152819","DOIUrl":null,"url":null,"abstract":"Due to the noise of various sources present in the interferograms and the differential interferograms, the detection of surface deformations by differential interferometry (DInSAR) is thereby a difficult and complicated task. However, the adoption of a visual inspection method for the measured results interpretation can lead to false interpretations, more often in the case of small deformations; that is due to the total dependence to the detection model conceiver. In this context, the work that we present is a contribution to the methods of surface deformation detection by DInSAR, the automatic process that we propose is based on a multi-scale analysis by the discrete wavelet transform. In order to establish this latter, we generated deformation signatures by simulating a surface subsidence and integrating them into pairs of ERS1 / ERS2 sensors images acquired in the region of Ouargla in Algeria.","PeriodicalId":325654,"journal":{"name":"Proceedings of the 6th International Conference on Telecommunications and Remote Sensing","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Telecommunications and Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3152808.3152819","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Due to the noise of various sources present in the interferograms and the differential interferograms, the detection of surface deformations by differential interferometry (DInSAR) is thereby a difficult and complicated task. However, the adoption of a visual inspection method for the measured results interpretation can lead to false interpretations, more often in the case of small deformations; that is due to the total dependence to the detection model conceiver. In this context, the work that we present is a contribution to the methods of surface deformation detection by DInSAR, the automatic process that we propose is based on a multi-scale analysis by the discrete wavelet transform. In order to establish this latter, we generated deformation signatures by simulating a surface subsidence and integrating them into pairs of ERS1 / ERS2 sensors images acquired in the region of Ouargla in Algeria.