30 October 2009 Multiscale graph cut based classification of urban hyperspectral imagery
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Proceedings Volume 7494, MIPPR 2009: Multispectral Image Acquisition and Processing; 74941U (2009) https://doi.org/10.1117/12.834010
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
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.
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Xin Yu, Xin Yu, Ruiqin Niu, Ruiqin Niu, Yi Wang, Yi Wang, Ke Wu, Ke Wu, } "Multiscale graph cut based classification of urban hyperspectral imagery", Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74941U (30 October 2009); doi: 10.1117/12.834010; https://doi.org/10.1117/12.834010
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