{"title":"提高雷达目标分类精度的信息保留预处理","authors":"H. Bozkurt, I. Erer","doi":"10.1109/RAST.2017.8003014","DOIUrl":null,"url":null,"abstract":"Edge preserving image decomposition is proposed as a preprocessing technique to increase the accuracy in automatic radar target classification. The radar images are decomposed trough edge preserving image decomposition methods with parameters optimized for a better classification rate. By appropriate choice of the parameters, it is possible to keep the necessary information in the residual images while transferring the redundant information to the detail planes. Thus the use of residual image with the meaningful amount of data without redundancies increases the classification performance. The proposed preprocessing scheme is validated for an experimental dataset and compared with other image decomposition methods.","PeriodicalId":434418,"journal":{"name":"2017 8th International Conference on Recent Advances in Space Technologies (RAST)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Information preserving preprocessing for improved radar target classification accuracy\",\"authors\":\"H. Bozkurt, I. Erer\",\"doi\":\"10.1109/RAST.2017.8003014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Edge preserving image decomposition is proposed as a preprocessing technique to increase the accuracy in automatic radar target classification. The radar images are decomposed trough edge preserving image decomposition methods with parameters optimized for a better classification rate. By appropriate choice of the parameters, it is possible to keep the necessary information in the residual images while transferring the redundant information to the detail planes. Thus the use of residual image with the meaningful amount of data without redundancies increases the classification performance. The proposed preprocessing scheme is validated for an experimental dataset and compared with other image decomposition methods.\",\"PeriodicalId\":434418,\"journal\":{\"name\":\"2017 8th International Conference on Recent Advances in Space Technologies (RAST)\",\"volume\":\"114 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 8th International Conference on Recent Advances in Space Technologies (RAST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAST.2017.8003014\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 8th International Conference on Recent Advances in Space Technologies (RAST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAST.2017.8003014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Information preserving preprocessing for improved radar target classification accuracy
Edge preserving image decomposition is proposed as a preprocessing technique to increase the accuracy in automatic radar target classification. The radar images are decomposed trough edge preserving image decomposition methods with parameters optimized for a better classification rate. By appropriate choice of the parameters, it is possible to keep the necessary information in the residual images while transferring the redundant information to the detail planes. Thus the use of residual image with the meaningful amount of data without redundancies increases the classification performance. The proposed preprocessing scheme is validated for an experimental dataset and compared with other image decomposition methods.