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Proceedings Paper

Multiscale graph cut based classification of urban hyperspectral imagery
Author(s): Xin Yu; Ruiqin Niu; Yi Wang; Ke Wu
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Paper Abstract

This paper proposes a novel multiscale graph cut based analysis framework for the supervised classification of hyperspectral imagery. This framework is aimed at obtaining accurate and reliable maps by properly considering the spatial-context information. It is made up of two main blocks: 1) a feature-extraction block exploits an object-oriented analysis and representation of hyperspectral imagery that is obtained by multiscale graph cut (MGC) based segmentation; 2) a classifier, based on support vector machines (SVMs), capable of analyzing hyperdimensional feature spaces. Experimental results confirm the effectiveness of the proposed system for the analysis of hyperspectral imagery.

Paper Details

Date Published: 30 October 2009
PDF: 9 pages
Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74941U (30 October 2009); doi: 10.1117/12.834010
Show Author Affiliations
Xin Yu, China Univ. of Geosciences (China)
Ruiqin Niu, China Univ. of Geosciences (China)
Yi Wang, China Univ. of Geosciences (China)
Ke Wu, China Univ. of Geosciences (China)

Published in SPIE Proceedings Vol. 7494:
MIPPR 2009: Multispectral Image Acquisition and Processing
Faxiong Zhang; Faxiong Zhang, Editor(s)

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