{"title":"基于Fisher准则的SAR图像分类器","authors":"Alexsandro M. Jacob, E. M. Hemerly, D. Fernandes","doi":"10.1109/SBRN.2002.1181464","DOIUrl":null,"url":null,"abstract":"A supervised neural classifier based on Fisher criterion is implemented to classify two regions in a real speckled SAR image. Regions around pre-classified pixels are presented to train the neural network that learns a sub-optimal set of masks via back-propagation algorithm. Classification performance is evaluated by using the ground truth. Results with higher than 90% of correct classification are obtained. The results are also compared with a statistical classifier based on Kullback-Liebler distance via the Kappa coefficient.","PeriodicalId":157186,"journal":{"name":"VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings.","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"SAR image classification using a neural classifier based on Fisher criterion\",\"authors\":\"Alexsandro M. Jacob, E. M. Hemerly, D. Fernandes\",\"doi\":\"10.1109/SBRN.2002.1181464\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A supervised neural classifier based on Fisher criterion is implemented to classify two regions in a real speckled SAR image. Regions around pre-classified pixels are presented to train the neural network that learns a sub-optimal set of masks via back-propagation algorithm. Classification performance is evaluated by using the ground truth. Results with higher than 90% of correct classification are obtained. The results are also compared with a statistical classifier based on Kullback-Liebler distance via the Kappa coefficient.\",\"PeriodicalId\":157186,\"journal\":{\"name\":\"VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings.\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SBRN.2002.1181464\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SBRN.2002.1181464","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SAR image classification using a neural classifier based on Fisher criterion
A supervised neural classifier based on Fisher criterion is implemented to classify two regions in a real speckled SAR image. Regions around pre-classified pixels are presented to train the neural network that learns a sub-optimal set of masks via back-propagation algorithm. Classification performance is evaluated by using the ground truth. Results with higher than 90% of correct classification are obtained. The results are also compared with a statistical classifier based on Kullback-Liebler distance via the Kappa coefficient.