3D Computation of Gray Level Co-occurrence in Hyperspectral Image Cubes

  • Fuan Tsai
  • Chun-Kai Chang
  • Jian-Yeo Rau
  • Tang-Huang Lin
  • Gin-Ron Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4679)


This study extended the computation of GLCM (gray level co-occurrence matrix) to a three-dimensional form. The objective was to treat hyperspectral image cubes as volumetric data sets and use the developed 3D GLCM computation algorithm to extract discriminant volumetric texture features for classification. As the kernel size of the moving box is the most important factor for the computation of GLCM-based texture descriptors, a three-dimensional semi-variance analysis algorithm was also developed to determine appropriate moving box sizes for 3D computation of GLCM from different data sets. The developed algorithms were applied to a series of classifications of two remote sensing hyperspectral image cubes and comparing their performance with conventional GLCM textural classifications. Evaluations of the classification results indicated that the developed semi-variance analysis was effective in determining the best kernel size for computing GLCM. It was also demonstrated that textures derived from 3D computation of GLCM produced better classification results than 2D textures.


Kernel Size Hyperspectral Imagery Minimum Noise Fraction Image Cube Hyperion Data 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Fuan Tsai
    • 1
  • Chun-Kai Chang
    • 1
  • Jian-Yeo Rau
    • 1
  • Tang-Huang Lin
    • 1
  • Gin-Ron Liu
    • 1
  1. 1.Center for Space and Remote Sensing Research, National Central University, 300 Zhong-Da Road, Zhongli, Taoyuan 320Taiwan

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