{"title":"Kirsch Direction Template Despeckling Algorithm of High-Resolution SAR Images-Based on Structural Information Detection","authors":"S. Hou, Zengguo Sun, Liu Yang, Yunjing Song","doi":"10.1109/LGRS.2020.2966369","DOIUrl":null,"url":null,"abstract":"In order to overcome the drawback of the traditional Kirsch template despeckling usings fixed windows, an improved Kirsch direction template despeckling algorithm, based on structural information detection, is proposed for high-resolution synthetic aperture radar (SAR) images. First, the point targets are detected and preserved in the current region. Second, the window is enlarged adaptively based on the statistical characteristics of the local region. Finally, the window finally obtained is classified. The averaged filter is directly adopted if the region is homogeneous, or else the Kirsch template filter is used. Combining point target detection, adaptive windowing, and region classification, altogether the proposed algorithm can effectively improve the performance of the traditional Kirsch direction template despeckling. Despeckling experiments on simulated and real high-resolution SAR images demonstrate that the Kirsch direction template despeckling algorithm based on structural information detection can not only sufficiently suppress speckle in homogenous and edge regions, but also effectively preserve point targets and edge information, leading to good despeckling results.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"177-181"},"PeriodicalIF":4.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2020.2966369","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Geoscience and Remote Sensing Letters","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/LGRS.2020.2966369","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 2
Abstract
In order to overcome the drawback of the traditional Kirsch template despeckling usings fixed windows, an improved Kirsch direction template despeckling algorithm, based on structural information detection, is proposed for high-resolution synthetic aperture radar (SAR) images. First, the point targets are detected and preserved in the current region. Second, the window is enlarged adaptively based on the statistical characteristics of the local region. Finally, the window finally obtained is classified. The averaged filter is directly adopted if the region is homogeneous, or else the Kirsch template filter is used. Combining point target detection, adaptive windowing, and region classification, altogether the proposed algorithm can effectively improve the performance of the traditional Kirsch direction template despeckling. Despeckling experiments on simulated and real high-resolution SAR images demonstrate that the Kirsch direction template despeckling algorithm based on structural information detection can not only sufficiently suppress speckle in homogenous and edge regions, but also effectively preserve point targets and edge information, leading to good despeckling results.
期刊介绍:
IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.