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Proceedings Paper

Unsupervised spectral-spatial classification of hyperspectral imagery using real and complex features and generalized histograms
Author(s): Julio M. Duarte-Carvajalino; Guillermo Sapiro; Miguel Velez-Reyes
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Paper 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.

Paper Details

Date Published: 11 April 2008
PDF: 12 pages
Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69660F (11 April 2008); doi: 10.1117/12.779142
Show Author Affiliations
Julio M. Duarte-Carvajalino, Univ. of Puerto Rico at Mayagüez (United States)
Guillermo Sapiro, Univ. of Minnesota (United States)
Miguel Velez-Reyes, Univ. of Puerto Rico at Mayagüez (United States)


Published in SPIE Proceedings Vol. 6966:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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