{"title":"基于ART2神经网络的SAR图像识别研究","authors":"Xiaoming Ye, Wei Gao, Yi Wang, Xiaoguang Hu","doi":"10.1109/ICIEA.2012.6361036","DOIUrl":null,"url":null,"abstract":"ART2 is a kind of self-organizing neural network which is based on adaptive resonance theory. It carries out the recognition by using competive learning and self-steady mechanism, and can learn by itself in dynamic environment with noise and without supervision. Its learning process can recognize learned models fastly and be adapted to new unknown objects rapidly. SAR ATR (Synthetic Aperture Radar Automatic Target Recognition) approach based on PCA and ART2 neural network is proposed in this paper. It takes the principal components as sample features, and then ART2 neural network is used to recognize SAR images. Experimental results with MSTAR SAR data sets show a better performance of recognition and generalization.","PeriodicalId":220747,"journal":{"name":"2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Research on SAR images recognition based on ART2 neural network\",\"authors\":\"Xiaoming Ye, Wei Gao, Yi Wang, Xiaoguang Hu\",\"doi\":\"10.1109/ICIEA.2012.6361036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ART2 is a kind of self-organizing neural network which is based on adaptive resonance theory. It carries out the recognition by using competive learning and self-steady mechanism, and can learn by itself in dynamic environment with noise and without supervision. Its learning process can recognize learned models fastly and be adapted to new unknown objects rapidly. SAR ATR (Synthetic Aperture Radar Automatic Target Recognition) approach based on PCA and ART2 neural network is proposed in this paper. It takes the principal components as sample features, and then ART2 neural network is used to recognize SAR images. Experimental results with MSTAR SAR data sets show a better performance of recognition and generalization.\",\"PeriodicalId\":220747,\"journal\":{\"name\":\"2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)\",\"volume\":\"68 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIEA.2012.6361036\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEA.2012.6361036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on SAR images recognition based on ART2 neural network
ART2 is a kind of self-organizing neural network which is based on adaptive resonance theory. It carries out the recognition by using competive learning and self-steady mechanism, and can learn by itself in dynamic environment with noise and without supervision. Its learning process can recognize learned models fastly and be adapted to new unknown objects rapidly. SAR ATR (Synthetic Aperture Radar Automatic Target Recognition) approach based on PCA and ART2 neural network is proposed in this paper. It takes the principal components as sample features, and then ART2 neural network is used to recognize SAR images. Experimental results with MSTAR SAR data sets show a better performance of recognition and generalization.