{"title":"光谱学和高光谱成像中的若干解混问题及算法","authors":"M. Berman","doi":"10.1109/AIPR.2006.37","DOIUrl":null,"url":null,"abstract":"The automated identification and mapping of the constituent materials in a hyperspectral image is a problem of considerable interest. A significant issue is that the spectra at many pixels in such an image are actually mixtures of the spectra of the pure constituents. I review methods of \"unmixing\" spectra into their pure constituents, both when a \"spectral library\" of the pure constituents is available, and where no such library is available. Our own algorithms in both these areas are exemplified with a mineral and a biological example.","PeriodicalId":375571,"journal":{"name":"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Some Unmixing Problems and Algorithms in Spectroscopy and Hyperspectral Imaging\",\"authors\":\"M. Berman\",\"doi\":\"10.1109/AIPR.2006.37\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The automated identification and mapping of the constituent materials in a hyperspectral image is a problem of considerable interest. A significant issue is that the spectra at many pixels in such an image are actually mixtures of the spectra of the pure constituents. I review methods of \\\"unmixing\\\" spectra into their pure constituents, both when a \\\"spectral library\\\" of the pure constituents is available, and where no such library is available. Our own algorithms in both these areas are exemplified with a mineral and a biological example.\",\"PeriodicalId\":375571,\"journal\":{\"name\":\"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-10-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIPR.2006.37\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIPR.2006.37","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Some Unmixing Problems and Algorithms in Spectroscopy and Hyperspectral Imaging
The automated identification and mapping of the constituent materials in a hyperspectral image is a problem of considerable interest. A significant issue is that the spectra at many pixels in such an image are actually mixtures of the spectra of the pure constituents. I review methods of "unmixing" spectra into their pure constituents, both when a "spectral library" of the pure constituents is available, and where no such library is available. Our own algorithms in both these areas are exemplified with a mineral and a biological example.