{"title":"Matched affine joint subspace detection in remote hyperspectral reconnaissance","authors":"A. Schaum","doi":"10.1109/AIPR.2002.1182249","DOIUrl":null,"url":null,"abstract":"The GLR (generalized likelihood ratio) test has been invoked for several decades as a prescription for generating target detection algorithms, when limited prior knowledge makes a theoretically ideal test inapplicable. Many popular HSI (hyperspectral imaging) detection algorithms rely ultimately on a GLR justification. However, experience with real-time remotely deployed detection systems indicates that certain heuristic modifications to the classic algorithm suite consistently produce better performance. A new target detection test, based on a Bayesian likelihood ratio (BLR) principle, has been used to explain these results and to define a broader class of detection algorithms. The more general approach facilitates the incorporation of prior beliefs, such as that gleaned from experience in measurement programs. A BLR test has been used to generate a new family of HSI algorithms, called matched affine joint subspace detection (MAJSD). Several examples from this class are described, and their utility is validated by detection comparisons.","PeriodicalId":379110,"journal":{"name":"Applied Imagery Pattern Recognition Workshop, 2002. Proceedings.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Imagery Pattern Recognition Workshop, 2002. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIPR.2002.1182249","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The GLR (generalized likelihood ratio) test has been invoked for several decades as a prescription for generating target detection algorithms, when limited prior knowledge makes a theoretically ideal test inapplicable. Many popular HSI (hyperspectral imaging) detection algorithms rely ultimately on a GLR justification. However, experience with real-time remotely deployed detection systems indicates that certain heuristic modifications to the classic algorithm suite consistently produce better performance. A new target detection test, based on a Bayesian likelihood ratio (BLR) principle, has been used to explain these results and to define a broader class of detection algorithms. The more general approach facilitates the incorporation of prior beliefs, such as that gleaned from experience in measurement programs. A BLR test has been used to generate a new family of HSI algorithms, called matched affine joint subspace detection (MAJSD). Several examples from this class are described, and their utility is validated by detection comparisons.