{"title":"SAR Change Detection Algorithm Combined with FFDNet Spatial Denoising","authors":"Yuqing Wu, Qing Xu, Zheng Zhang, Jingzhen Ma, Tianming Zhao, Xinming Zhu","doi":"10.30564/jees.v5i2.5980","DOIUrl":null,"url":null,"abstract":"Objectives: When detecting changes in synthetic aperture radar (SAR) images, the quality of the difference map has an important impact on the detection results, and the speckle noise in the image interferes with the extraction of change information. In order to improve the detection accuracy of SAR image change detection and improve the quality of the difference map, this paper proposes a method that combines the popular deep neural network with the clustering algorithm. Methods: Firstly, the SAR image with speckle noise was constructed, and the FFDNet architecture was used to retrain the SAR image, and the network parameters with better effect on speckle noise suppression were obtained. Then the log ratio operator is generated by using the reconstructed image output from the network. Finally, K-means and FCM clustering algorithms are used to analyze the difference images, and the binary map of change detection results is generated. Results: The experimental results have high detection accuracy on Bern and Sulzberger's real data, which proves the effectiveness of the method.","PeriodicalId":55272,"journal":{"name":"Carpathian Journal of Earth and Environmental Sciences","volume":"15 6","pages":"0"},"PeriodicalIF":0.9000,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Carpathian Journal of Earth and Environmental Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30564/jees.v5i2.5980","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: When detecting changes in synthetic aperture radar (SAR) images, the quality of the difference map has an important impact on the detection results, and the speckle noise in the image interferes with the extraction of change information. In order to improve the detection accuracy of SAR image change detection and improve the quality of the difference map, this paper proposes a method that combines the popular deep neural network with the clustering algorithm. Methods: Firstly, the SAR image with speckle noise was constructed, and the FFDNet architecture was used to retrain the SAR image, and the network parameters with better effect on speckle noise suppression were obtained. Then the log ratio operator is generated by using the reconstructed image output from the network. Finally, K-means and FCM clustering algorithms are used to analyze the difference images, and the binary map of change detection results is generated. Results: The experimental results have high detection accuracy on Bern and Sulzberger's real data, which proves the effectiveness of the method.
期刊介绍:
The publishing of CARPATHIAN JOURNAL of EARTH and ENVIRONMENTAL SCIENCES has started in 2006. The regularity of this magazine is biannual. The magazine will publish scientific works, in international purposes, in different areas of research, such as : geology, geography, environmental sciences, the environmental pollution and protection, environmental chemistry and physic, environmental biodegradation, climatic exchanges, fighting against natural disasters, protected areas, soil degradation, water quality, water supplies, sustainable development.