Pub Date : 2003-10-27DOI: 10.1109/WARSD.2003.1295165
J. Richards
A review is given of the development of the field of image understanding in remote sensing, with an emphasis on the contributions of David Landgrebe and his group at the Laboratory for Applications of Remote Sensing, Purdue University. The differences in approach required for multispectral, hyperspectral and radar image data are emphasised, in which the seminal contributions to the field by Landgrebe as well as others are summarised. The treatment concludes by examining the current problem of thematic mapping from mixed spatial data types, such as would be found in a geographical information system. Rather than seeking techniques that "fuse" available data types as a means for deriving joint inferences, it is proposed instead that the most practical means is to have each individual data source analysed separately by the most appropriate techniques and the fuse at the label level using the facilities of an expert consultant.
{"title":"Information and understanding: analysis of remotely sensed data","authors":"J. Richards","doi":"10.1109/WARSD.2003.1295165","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295165","url":null,"abstract":"A review is given of the development of the field of image understanding in remote sensing, with an emphasis on the contributions of David Landgrebe and his group at the Laboratory for Applications of Remote Sensing, Purdue University. The differences in approach required for multispectral, hyperspectral and radar image data are emphasised, in which the seminal contributions to the field by Landgrebe as well as others are summarised. The treatment concludes by examining the current problem of thematic mapping from mixed spatial data types, such as would be found in a geographical information system. Rather than seeking techniques that \"fuse\" available data types as a means for deriving joint inferences, it is proposed instead that the most practical means is to have each individual data source analysed separately by the most appropriate techniques and the fuse at the label level using the facilities of an expert consultant.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131166972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-10-27DOI: 10.1109/WARSD.2003.1295203
Runhe Shi, D. Zhuang, Z. Niu
The prediction of leaf biochemical concentrations with hyperspectral data is one of latest research directions in hyperspectral remote sensing. Statistical modeling being a convenient and common-used method, spectral transformations are always performed as its preprocess. We discussed several usual transformations including full-band based transformations such as reciprocal, logarithm, and derivative spectra, and one-absorption-feature based transformation: continuum removal. The effects of those transformations on the prediction of C/N were compared using correlation analyses and stepwise regressions. Results show that the effect of continuum removal is the best, which is physically based and not site-specific at all.
{"title":"Effects of spectral transformations in statistical modeling of leaf biochemical concentrations","authors":"Runhe Shi, D. Zhuang, Z. Niu","doi":"10.1109/WARSD.2003.1295203","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295203","url":null,"abstract":"The prediction of leaf biochemical concentrations with hyperspectral data is one of latest research directions in hyperspectral remote sensing. Statistical modeling being a convenient and common-used method, spectral transformations are always performed as its preprocess. We discussed several usual transformations including full-band based transformations such as reciprocal, logarithm, and derivative spectra, and one-absorption-feature based transformation: continuum removal. The effects of those transformations on the prediction of C/N were compared using correlation analyses and stepwise regressions. Results show that the effect of continuum removal is the best, which is physically based and not site-specific at all.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127387412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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