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Proceedings Paper

Spectral-spatial classification to pattern recognition of hyperspectral imagery
Author(s): Tung-Ching Su
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Paper Abstract

Recently several spectral-spatial classification methods had been presented and applied to pattern recognition of hyperspectral imagery. However, the present methods are especially suitable for classifying images with large spatial structures in spite of the derived classification accuracies of above 90%. To classify hyperspectral images with larger as well as smaller spatial structures, a novel spectral-spatial classification method was presented and tested on an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) image with 145×145 pixels and 220 bands. Firstly, the AVIRIS image was implemented a spectral mixture analysis using minimum noise fraction (MNF). Based on the obtained n-dimensional eigenimage, support vector machine (SVM) was used to classify the AVIRIS image. Simultaneously, the eigenimage was calculated the mathematical morphology-based image gradients for the n dimensions so to obtain n watershed segmentation images. Finally, the SVM classification map was turned into several new ones through a series of post-processing. The experimental results verify that the proposed spectral-spatial classification method has the capability to detect larger as well as smaller spatial structures in hyperspectral imagery.

Paper Details

Date Published: 2 June 2012
PDF: 5 pages
Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 83341F (2 June 2012); doi: 10.1117/12.946597
Show Author Affiliations
Tung-Ching Su, National Quemoy Univ. (Taiwan)


Published in SPIE Proceedings Vol. 8334:
Fourth International Conference on Digital Image Processing (ICDIP 2012)
Mohamed Othman; Sukumar Senthilkumar; Xie Yi, Editor(s)

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