SAR Target Discrimination Algorithm Based on Bag-of-words Model with Multi-feature Fusion
SONG Wenqing WANG Yinghua SHI Lihui LIU Hongwei BAO Zheng
(National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China)
(Collaborative Innovation Center of Information Sensing and Understanding at Xidian University, Xi’an 710071, China)
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.
宋文青,王英华,时荔蕙,刘宏伟,保铮. 基于多特征融合词包模型的SAR目标鉴别算法[J]. 电子与信息学报, 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. JEIT, 2017, 39(11): 2705-2715.
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