Feature extraction of hyperspectral image cubes using three-dimensional gray-level cooccurrence

Fuan Tsai, Jhe Syuan Lai

Research output: Contribution to journalArticlepeer-review

67 Scopus citations


This paper presents a novel approach for the feature extraction of hyperspectral image cubes. In this paper, hyperspectral image cubes are treated as volumetric data sets. Features that are most helpful in separating different targets are effectively extracted from the hyperspectral image cubes using a newly developed high-order texture analysis method. The traditional texture measure of the gray-level cooccurrence matrix is extended to a 3-D tensor field to explore the complicated volumetric data more effectively and to extract discriminant features for better classification. As the kernel size is one of the most important parameters in statistics-based texture analysis, a semivariance analysis and a spectral separability measure are used to determine the most appropriate kernel size in the spatial and spectral domains, respectively, for computing 3-D gray-level cooccurrence. In addition, a few statistical indexes are also extended to third-order forms in order to calculate quantitative texture properties of the generated cooccurrence tensor field. An airborne hyperspectral data set and an EO-1 Hyperion image are used to test the performance of the developed algorithms. Experimental results indicate that the developed 3-D texture analysis outperforms conventional second-order texture descriptors and the support vector machine-based classifier in supervised classifications of both hyperspectral data sets.

Original languageEnglish
Article number6410025
Pages (from-to)3504-3513
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number6
StatePublished - 2013


  • Gray-level cooccurrence
  • hyperspectral
  • three-dimensional texture analysis
  • volumetric data


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