{"title":"基于空间上下文信息的图像分类方法与现有图像分类方法的比较","authors":"Houda Hassouna","doi":"10.22436/mns.05.01.02","DOIUrl":null,"url":null,"abstract":"In this paper, we present the results obtained for the remote sensing image classification by using three methods of classification namely, Gaussian process classification method (GPC), morphological profile for classification method (MPC) and spatial contextual Gaussian process classification method (SGPC). Several classification approaches have shown that the exploitation of spatial contextual information can be attractive to increase the classification accuracy by introducing a new automated learning approach based on Gaussian process theory.","PeriodicalId":443718,"journal":{"name":"Mathematics in Natural Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparaison between the method which is used the spatial contextual information and some methods of image classification\",\"authors\":\"Houda Hassouna\",\"doi\":\"10.22436/mns.05.01.02\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present the results obtained for the remote sensing image classification by using three methods of classification namely, Gaussian process classification method (GPC), morphological profile for classification method (MPC) and spatial contextual Gaussian process classification method (SGPC). Several classification approaches have shown that the exploitation of spatial contextual information can be attractive to increase the classification accuracy by introducing a new automated learning approach based on Gaussian process theory.\",\"PeriodicalId\":443718,\"journal\":{\"name\":\"Mathematics in Natural Science\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mathematics in Natural Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22436/mns.05.01.02\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematics in Natural Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22436/mns.05.01.02","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparaison between the method which is used the spatial contextual information and some methods of image classification
In this paper, we present the results obtained for the remote sensing image classification by using three methods of classification namely, Gaussian process classification method (GPC), morphological profile for classification method (MPC) and spatial contextual Gaussian process classification method (SGPC). Several classification approaches have shown that the exploitation of spatial contextual information can be attractive to increase the classification accuracy by introducing a new automated learning approach based on Gaussian process theory.