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Proceedings Paper

An effective bag-of-visual-words framework for SAR image classification
Author(s): Jie Feng; L. C. Jiao; Xiangrong Zhang; Ruican Niu
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Paper Abstract

The difficulty existing in synthetic aperture radar (SAR) image classification is large amounts of unpredictable and inestimable speckle, leading to degradation of the image quality and concealing important objectives of interest. By exploiting an efficient image features extraction technique, bag-of-visual-words (BOV) for its ability of 'midlevel' feature representation, and a new developed non-local (NL-) means denosing method suitable for multiplicative speckle, we present a novel and effective BOV framework for SAR image classification. Compared with the other two representative algorithms, the experimental results show that the proposed algorithm has obtained more satisfactory and cogent classification performance and performed more robustness to SAR speckle.

Paper Details

Date Published: 23 November 2011
PDF: 5 pages
Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 800606 (23 November 2011); doi: 10.1117/12.900579
Show Author Affiliations
Jie Feng, Xidian Univ. (China)
L. C. Jiao, Xidian Univ. (China)
Xiangrong Zhang, Xidian Univ. (China)
Ruican Niu, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 8006:
MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Faxiong Zhang; Faxiong Zhang, Editor(s)

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