An effective bag-of-visual-words framework for SAR image classification

Jie Feng; L. C. Jiao; Xiangrong Zhang; Ruican Niu

doi:10.1117/12.900579

23 November 2011 An effective bag-of-visual-words framework for SAR image classification

Jie Feng, L. C. Jiao, Xiangrong Zhang, Ruican Niu

Proceedings Volume 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 800606 (2011) https://doi.org/10.1117/12.900579
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

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.

Citation Download Citation

Jie Feng, L. C. Jiao, Xiangrong Zhang, and Ruican Niu "An effective bag-of-visual-words framework for SAR image classification", Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 800606 (23 November 2011); https://doi.org/10.1117/12.900579

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KEYWORDS

Synthetic aperture radar

Image classification

Speckle

Visualization

Nickel

Algorithm development

Feature extraction

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