Multiscale graph cut based classification of urban hyperspectral imagery

Xin Yu; Ruiqin Niu; Yi Wang; Ke Wu

doi:10.1117/12.834010

30 October 2009 Multiscale graph cut based classification of urban hyperspectral imagery

Xin Yu, Ruiqin Niu, Yi Wang, Ke Wu

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.

Citation Download Citation

Xin Yu, Ruiqin Niu, Yi Wang, and 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); https://doi.org/10.1117/12.834010

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KEYWORDS

Image segmentation

Hyperspectral imaging

Image classification

Feature extraction

Binary data

Roads

Remote sensing

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