A review of unsupervised spectral target analysis for hyperspectral imagery

  • Authors:

  • Chein-I Chang;Xiaoli Jiao;Chao-Cheng Wu;Yingzi Du;Mann-Li Chang

  • Affiliations:

  • Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD and Department of Electrical Engineering, Na ...;Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD;Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD;Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN;Management and Information Department, Kang Ning Nursing and Management Junior College, Taipei, Taiwan

  • Venue:
  • EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
  • Year:
  • 2010

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Abstract

One of great challenges in unsupervised hyperspectral target analysis is how to obtain desired knowledge in an unsupervisedmeans 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.