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Proceedings Article

Unsupervised spectral-spatial classification of hyperspectral imagery using real and complex features and generalized histograms

[+] Author Affiliations
Julio M. Duarte-Carvajalino, Miguel Velez-Reyes

Univ. of Puerto Rico at Mayagüez

Guillermo Sapiro

Univ. of Minnesota

Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69660F (April 11, 2008); doi:10.1117/12.779142
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From Conference Volume 6966

  • Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV
  • Sylvia S. Shen; Paul E. Lewis
  • Orlando, FL | March 16, 2008

abstract

In this work, we study unsupervised classification algorithms for hyperspectral images based on band-by-band scalar histograms and vector-valued generalized histograms, obtained by vector quantization. The corresponding histograms are compared by dissimilarity metrics such as the chi-square, Kolmogorov-Smirnorv, and earth mover's distances. The histograms are constructed from homogeneous regions in the images identified by a pre-segmentation algorithm and distance metrics between pixels. We compare the traditional spectral-only segmentation algorithms C-means and ISODATA, versus spectral-spatial segmentation algorithms such as unsupervised ECHO and a novel segmentation algorithm based on scale-space concepts. We also evaluate the use of complex features consisting of the real spectrum and its derivative as the imaginary part. The comparison between the different segmentation algorithms and distance metrics is based on their unsupervised classification accuracy using three real hyperspectral images with known ground truth.

© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Julio M. Duarte-Carvajalino ; Guillermo Sapiro and Miguel Velez-Reyes
"Unsupervised spectral-spatial classification of hyperspectral imagery using real and complex features and generalized histograms", Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69660F (April 11, 2008); doi:10.1117/12.779142; http://dx.doi.org/10.1117/12.779142


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