基于多特征融合词包模型的SAR目标鉴别算法

doi:10.11999/JEIT170086

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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.

Key words： SAR Target discrimination Bag-of-Words (BoW) model Low-level descriptor Multiple Kernel Learning (MKL)

Received: 23 January 2017 Published: 13 September 2017

PACS:

TN958

Fund: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)

Corresponding Authors: WANG Yinghua E-mail: yhwang@xidian.edu.cn

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	SONG Wenqing
	WANG Yinghua
	SHI Lihui
	LIU Hongwei
	BAO Zheng

Cite this article:

SONG Wenqing,WANG Yinghua,SHI Lihui等. SAR Target Discrimination Algorithm Based on Bag-of-words Model with Multi-feature Fusion[J]. JEIT, 2017, 39(11): 2705-2715.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT170086 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I11/2705

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