{"title":"基于图的知识发现与图像分割层次的集成,用于数据分析、数据挖掘和知识发现","authors":"J. Tilton, D. Cook, Nikhil S. Ketkar","doi":"10.1109/IGARSS.2008.4779391","DOIUrl":null,"url":null,"abstract":"Currently available pixel-based image analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. We are exploring an approach to object-based image analysis in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software are analyzed by the Subdue graph-based knowledge-discovery system. In this paper we discuss our initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and discuss results from real and simulated data.","PeriodicalId":237798,"journal":{"name":"IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"The Integraton of Graph-Based Knowledge Discovery with Image Segmentation Hierarchies for Data Analysis, Data Mining and Knowledge Discovery\",\"authors\":\"J. Tilton, D. Cook, Nikhil S. Ketkar\",\"doi\":\"10.1109/IGARSS.2008.4779391\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Currently available pixel-based image analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. We are exploring an approach to object-based image analysis in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software are analyzed by the Subdue graph-based knowledge-discovery system. In this paper we discuss our initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and discuss results from real and simulated data.\",\"PeriodicalId\":237798,\"journal\":{\"name\":\"IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium\",\"volume\":\"77 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IGARSS.2008.4779391\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS.2008.4779391","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Integraton of Graph-Based Knowledge Discovery with Image Segmentation Hierarchies for Data Analysis, Data Mining and Knowledge Discovery
Currently available pixel-based image analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. We are exploring an approach to object-based image analysis in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software are analyzed by the Subdue graph-based knowledge-discovery system. In this paper we discuss our initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and discuss results from real and simulated data.