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Dimensionality Reduction of Hyperspectral Imagery Data for FeatureClassification

In: Handbook of Geomathematics

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  • Charles K. Chui

    (University of Missouri, Department of Mathematics
    Stanford University, Department of Statistics)

  • Jianzhong Wang

    (Sam Houston State University, Department of Mathematics)

Abstract

The objective of this chapter is to highlight the current research activities and recent progress in the area of dimensionality reduction of hyperspectral geological/geographical imagery data, which are widely used in image segmentation and feature classification. We will only focus on four topics of interest, namely hyperspectral image (HSI) data preprocessing, similarity/dissimilarity definition of HSI data, construction of dimensionality reduction (DR) kernels for HSI data, and HSI data dimensionality reduction algorithms based on DR kernels.

Suggested Citation

  • Charles K. Chui & Jianzhong Wang, 2010. "Dimensionality Reduction of Hyperspectral Imagery Data for FeatureClassification," Springer Books, in: Willi Freeden & M. Zuhair Nashed & Thomas Sonar (ed.), Handbook of Geomathematics, chapter 34, pages 1005-1047, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-01546-5_34
    DOI: 10.1007/978-3-642-01546-5_34
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