{"title":"航空光谱图像中基于物理的模型获取与识别","authors":"D. Slater, G. Healey","doi":"10.1109/ICCV.2001.937633","DOIUrl":null,"url":null,"abstract":"We consider the problem of acquiring models for unknown materials in airborne 0.4 /spl mu/m-2.5 /spl mu/m hyperspectral imagery and using these models to identify the unknown materials an image data obtained under significantly different conditions. The material models are generated using an airborne sensor spectrum measured under unknown conditions and a physical model for spectral variability. For computational efficiency, the material models are represented using low-dimensional spectral subspaces. We demonstrate the effectiveness of the material models using a set of material tracking experiments in HYDICE images acquired in a forest environment over widely varying conditions. We show that techniques based on the new representation significantly outperform methods based on direct spectral matching.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"108 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Physics-based model acquisition and identification in airborne spectral images\",\"authors\":\"D. Slater, G. Healey\",\"doi\":\"10.1109/ICCV.2001.937633\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider the problem of acquiring models for unknown materials in airborne 0.4 /spl mu/m-2.5 /spl mu/m hyperspectral imagery and using these models to identify the unknown materials an image data obtained under significantly different conditions. The material models are generated using an airborne sensor spectrum measured under unknown conditions and a physical model for spectral variability. For computational efficiency, the material models are represented using low-dimensional spectral subspaces. We demonstrate the effectiveness of the material models using a set of material tracking experiments in HYDICE images acquired in a forest environment over widely varying conditions. We show that techniques based on the new representation significantly outperform methods based on direct spectral matching.\",\"PeriodicalId\":429441,\"journal\":{\"name\":\"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001\",\"volume\":\"108 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCV.2001.937633\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCV.2001.937633","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Physics-based model acquisition and identification in airborne spectral images
We consider the problem of acquiring models for unknown materials in airborne 0.4 /spl mu/m-2.5 /spl mu/m hyperspectral imagery and using these models to identify the unknown materials an image data obtained under significantly different conditions. The material models are generated using an airborne sensor spectrum measured under unknown conditions and a physical model for spectral variability. For computational efficiency, the material models are represented using low-dimensional spectral subspaces. We demonstrate the effectiveness of the material models using a set of material tracking experiments in HYDICE images acquired in a forest environment over widely varying conditions. We show that techniques based on the new representation significantly outperform methods based on direct spectral matching.