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.


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    Title :

    Matched affine joint subspace detection in remote hyperspectral reconnaissance


    Contributors:


    Publication date :

    2002-01-01


    Size :

    270060 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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