Mengjie Zhou, Guofeng Zhang, Xiaoguang Hu, Dan Sun, Jin Xiao
{"title":"SAR real-time guidance system based on multi-scale FAST-BRISK","authors":"Mengjie Zhou, Guofeng Zhang, Xiaoguang Hu, Dan Sun, Jin Xiao","doi":"10.1109/ICCA.2017.8003098","DOIUrl":null,"url":null,"abstract":"In recent years, SAR (Synthetic Aperture Radar) has been widely used in the real-time guidance system. As the foundation of system, SAR image registration directly affects the guidance performance. In order to achieve the high precision and low computation, in this paper, we adopt well-known FAST (Features from Accelerated Segment Test) for feature detection and BRISK (Binary Robust Invariant Scalable Keypoints) for feature description. Then, we use Hamming distance and RANSAC (Random Sample Consensus) to match key points and estimate transformation parameters. To evaluate the performance of algorithms, three simulation experiments have been accomplished. The results and comparative analysis show a better performance of our proposed method.","PeriodicalId":379025,"journal":{"name":"2017 13th IEEE International Conference on Control & Automation (ICCA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th IEEE International Conference on Control & Automation (ICCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCA.2017.8003098","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In recent years, SAR (Synthetic Aperture Radar) has been widely used in the real-time guidance system. As the foundation of system, SAR image registration directly affects the guidance performance. In order to achieve the high precision and low computation, in this paper, we adopt well-known FAST (Features from Accelerated Segment Test) for feature detection and BRISK (Binary Robust Invariant Scalable Keypoints) for feature description. Then, we use Hamming distance and RANSAC (Random Sample Consensus) to match key points and estimate transformation parameters. To evaluate the performance of algorithms, three simulation experiments have been accomplished. The results and comparative analysis show a better performance of our proposed method.