{"title":"多光谱图像多尺度区域分类的层次马尔可夫模型","authors":"A. Katartzis, I. Vanhamel, H. Sahli","doi":"10.1109/WARSD.2003.1295223","DOIUrl":null,"url":null,"abstract":"We propose a new multispectral image classification method, based on a Markovian model, defined on the hierarchy of a multiscale region adjacency graph. The paper describes the main principles of our method and illustrates classification results on a set of artificial and remote sensing images, together with qualitative and quantitative comparisons with a variety of multi-and single-resolution Bayesian classification approaches.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A hierarchical Markovian model for multiscale region-based classification of multispectral images\",\"authors\":\"A. Katartzis, I. Vanhamel, H. Sahli\",\"doi\":\"10.1109/WARSD.2003.1295223\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a new multispectral image classification method, based on a Markovian model, defined on the hierarchy of a multiscale region adjacency graph. The paper describes the main principles of our method and illustrates classification results on a set of artificial and remote sensing images, together with qualitative and quantitative comparisons with a variety of multi-and single-resolution Bayesian classification approaches.\",\"PeriodicalId\":395735,\"journal\":{\"name\":\"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-10-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WARSD.2003.1295223\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WARSD.2003.1295223","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A hierarchical Markovian model for multiscale region-based classification of multispectral images
We propose a new multispectral image classification method, based on a Markovian model, defined on the hierarchy of a multiscale region adjacency graph. The paper describes the main principles of our method and illustrates classification results on a set of artificial and remote sensing images, together with qualitative and quantitative comparisons with a variety of multi-and single-resolution Bayesian classification approaches.