SAR Target Discrimination Algorithm Based on Bag-of-words Model with Multi-feature Fusion

SONG Wenqing; WANG Yinghua; SHI Lihui; LIU Hongwei; BAO Zheng

doi:10.11999/JEIT170086

Volume 39 Issue 11

Nov. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(11): 2705-2715

SONG Wenqing, WANG Yinghua, SHI Lihui, LIU Hongwei, BAO Zheng. SAR Target Discrimination Algorithm Based on Bag-of-words Model with Multi-feature Fusion[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2705-2715. doi: 10.11999/JEIT170086

Citation:

SONG Wenqing, WANG Yinghua, SHI Lihui, LIU Hongwei, BAO Zheng. SAR Target Discrimination Algorithm Based on Bag-of-words Model with Multi-feature Fusion[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2705-2715. doi: 10.11999/JEIT170086

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SAR Target Discrimination Algorithm Based on Bag-of-words Model with Multi-feature Fusion

doi: 10.11999/JEIT170086

Funds:

The National Natural Science Foundation of China (61671354, 61701379), The National Science Fund for Distinguished Young Scholars of China (61525105), The Fundamental Research Funds for the Central Universities, The Natural Science Basic Research Plan in Shaanxi Province of China (2016JQ6048)

Received Date: 2017-01-23
Rev Recd Date: 2017-08-25
Publish Date: 2017-11-19

Abstract

Abstract

In order to solve the SAR target discrimination problem in the real complex scenes, a SAR target discrimination method is proposed based on Bag-of-Words (BoW) model with multiple low-level features fusion. In the low-level feature extraction stage of BoW model, the SAR-SIFT feature is utilized to describe the shape information of local regions of an image sample. And also, a set of new local descriptors is used to capture the contrast information and the texture information of the local regions, which is extracted based on the traditional target discrimination features. For the fusion of different low-level features in BoW model, the image-level feature fusion strategy is implemented to generate the image global feature, which is realized by the Multiple Kernel Learning (MKL) method with L2-norm regularization. Experimental results with the MiniSAR real SAR dataset show that the proposed SAR target discrimination algorithm based on BoW model with multi-feature fusion achieves better discrimination performance compared with methods based on the traditional discrimination features and the BoW model features using single low-level descriptor.
- SAR,
- Target discrimination,
- Bag-of-Words (BoW) model,
- Low-level descriptor,
- Multiple Kernel Learning (MKL)

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References(36)

References

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