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Proceedings Paper

Incorporating spectral, texture, and shape information for high spatial resolution satellite imagery classification
Author(s): Yindi Zhao; Peijun Du
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Paper Abstract

Texture and shape analysis offer interesting possibilities to characterize the structural heterogeneity of classes in the high spatial resolution satellite imagery. In this paper, texture features are generated based on the Gaussian Markov random field (GMRF) model, and shape features are measured using geometric moments. Then feature selection is implemented according to the class separability. To reduce the border blurring effect introduced by texture features, the unsupervised classification algorithm involved ordered procedures is proposed, in which linear objects are extracted using spectral and shape features firstly, then other objects are detected using the combination of spectral, texture, and shape features. The proposed classification method is implemented using QuickBird imagery. For comparison, the standard K-means method with spectral data is used as a benchmark. The experimental results show that the ordered classification method with the combination of spectral, texture, and shape information performed better than conventional methods.

Paper Details

Date Published: 7 November 2008
PDF: 8 pages
Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 714719 (7 November 2008); doi: 10.1117/12.813246
Show Author Affiliations
Yindi Zhao, China Univ. of Mining and Technology (China)
Jiangsu Key Lab. of Resources and Environmental Information Engineering (China)
Peijun Du, China Univ. of Mining and Technology (China)


Published in SPIE Proceedings Vol. 7147:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang, Editor(s)

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