Unsupervised target subpixel detection in hyperspectral imagery

Chein-I Chang; Qian Du; Shao-Shan Chiang; Daniel C. Heinz; Irving W. Ginsberg

doi:10.1117/12.437027

20 August 2001 Unsupervised target subpixel detection in hyperspectral imagery

Chein-I Chang, Qian Du, Shao-Shan Chiang, Daniel C. Heinz, Irving W. Ginsberg

Proceedings Volume 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII; (2001) https://doi.org/10.1117/12.437027
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

Chein-I Chang, Qian Du, Shao-Shan Chiang, Daniel C. Heinz, and Irving W. Ginsberg "Unsupervised target subpixel detection in hyperspectral imagery", Proc. SPIE 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, (20 August 2001); https://doi.org/10.1117/12.437027

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