20 August 2001 Unsupervised target subpixel detection in hyperspectral imagery
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Abstract
Most subpixel detection approaches require either full or partial prior target knowledge. In many practical applications, such prior knowledge is generally very difficult to obtain, if not impossible. One way to remedy this situation is to obtain target information directly from the image data in an unsupervised manner. In this paper, unsupervised target subpixel detection is considered. Three unsupervised learning algorithms are proposed, which are the unsupervised vector quantization (UVQ) algorithm, unsupervised target generation process (UTGP) and unsupervised NCLS (UNCLS) algorithm. These algorithms produce necessary target information from the image data with no prior information required. Such generated target information is referred to as a posteriori target information and can be used to perform target detection.
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Chein-I Chang, Qian Du, Shao-Shan Chiang, Daniel C. Heinz, Irving W. Ginsberg, "Unsupervised target subpixel detection in hyperspectral imagery", Proc. SPIE 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, (20 August 2001); doi: 10.1117/12.437027; https://doi.org/10.1117/12.437027
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