A Review of Unsupervised Spectral Target Analysis for Hyperspectral Imagery

One of great challenges in unsupervised hyperspectral target analysis is how to obtain desired knowledge in an unsupervised means directly from the data for image analysis. This paper provides a review of unsupervised target analysis by first addressing two fundamental issues, "what are material substances of interest, referred to as targets?" and "how can these targets be extracted from the data?" and then further developing least squares (LS)-based unsupervised algorithms for finding spectral targets for analysis. In order to validate and substantiate the proposed unsupervised hyperspectral target analysis, three applications in endmember extraction, target detection and linear spectral unmixing are considered where custom-designed synthetic images and real image scenes are used to conduct experiments.

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Authors and Affiliations

1. Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD, 21250, USA

   Chein-I Chang, Xiaoli Jiao & Chao-Cheng Wu

2. Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan

   Chein-I Chang

3. Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA

   Yingzi Du

4. Management and Information Department, Kang Ning Nursing and Management Junior College, Taipei, Taiwan

   Mann-Li Chang

Corresponding author

Correspondence to Chein-I Chang.

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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