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.
Matched affine joint subspace detection in remote hyperspectral reconnaissance
2002-01-01
270060 byte
Conference paper
Electronic Resource
English
Matched Affine Joint Subspace Detection in Remote Hyperspectral Reconnaissance
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