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Proceedings Paper

Satellite image scene classification using spatial information
Author(s): Weiwei Song; Dunwei Wen; Ke Wang; Tong Liu; Mujun Zang
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Paper Abstract

In order to enhance the local feature’s describing capacity and improve the classification performance of high-resolution (HR) satellite images, we present an HR satellite image scene classification method that make use of spatial information of local feature. First, the spatial pyramid matching model (SPMM) is adopted to encode spatial information of local feature. Then, images are represented by the local feature descriptors and encoding information. Finally, the support vector machine (SVM) classifier is employed to classify image scenes. The experiment results on a real satellite image dataset show that our method can classify the scene classes with an 82.6% accuracy, which indicates that the method can work well on describing HR satellite images and classifying different scenes.

Paper Details

Date Published: 4 March 2015
PDF: 5 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94431K (4 March 2015); doi: 10.1117/12.2178739
Show Author Affiliations
Weiwei Song, Jilin Univ. (China)
Dunwei Wen, Athabasca Univ. (Canada)
Ke Wang, Jilin Univ. (China)
Tong Liu, Jilin Univ. (China)
Mujun Zang, Jilin Univ. (China)

Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)

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