{"title":"基于pc的光谱图像显示策略","authors":"J. Tyo, R. C. Olsen","doi":"10.1109/WARSD.2003.1295205","DOIUrl":null,"url":null,"abstract":"We present a new pseudocolor mapping strategy for use with spectral imagery based on a principal components analysis of spectral data. The mapping capitalizes on the similarities between human vision and hyperspectral data. The transformation results in final images where the color assigned to each pixel is solely determined by the position within the data cloud. Materials with similar spectral characteristics are presented in similar hues. This display strategy can be the first step in a supervised classification and clustering method.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"190 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"PC-based display strategy for spectral imagery\",\"authors\":\"J. Tyo, R. C. Olsen\",\"doi\":\"10.1109/WARSD.2003.1295205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a new pseudocolor mapping strategy for use with spectral imagery based on a principal components analysis of spectral data. The mapping capitalizes on the similarities between human vision and hyperspectral data. The transformation results in final images where the color assigned to each pixel is solely determined by the position within the data cloud. Materials with similar spectral characteristics are presented in similar hues. This display strategy can be the first step in a supervised classification and clustering method.\",\"PeriodicalId\":395735,\"journal\":{\"name\":\"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003\",\"volume\":\"190 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-10-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"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.2003.1295205\",\"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.2003.1295205","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present a new pseudocolor mapping strategy for use with spectral imagery based on a principal components analysis of spectral data. The mapping capitalizes on the similarities between human vision and hyperspectral data. The transformation results in final images where the color assigned to each pixel is solely determined by the position within the data cloud. Materials with similar spectral characteristics are presented in similar hues. This display strategy can be the first step in a supervised classification and clustering method.