Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder
AbstractFeature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE). The detailed procedure presented in this paper can be summarized as follows: firstly, 23 baseline features and Three-Patch Local Binary Pattern (TPLBP) features are extracted. These features can describe the global and local aspects of the image with less redundancy and more complementarity, providing richer information for feature fusion. Secondly, an effective feature fusion network is designed. Baseline and TPLBP features are cascaded and fed into a SAE. Then, with an unsupervised learning algorithm, the SAE is pre-trained by greedy layer-wise training method. Capable of feature expression, SAE makes the fused features more distinguishable. Finally, the model is fine-tuned by a softmax classifier and applied to the classification of targets. 10-class SAR targets based on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset got a classification accuracy up to 95.43%, which verifies the effectiveness of the presented algorithm. View Full-Text
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Kang, M.; Ji, K.; Leng, X.; Xing, X.; Zou, H. Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder. Sensors 2017, 17, 192.
Kang M, Ji K, Leng X, Xing X, Zou H. Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder. Sensors. 2017; 17(1):192.Chicago/Turabian Style
Kang, Miao; Ji, Kefeng; Leng, Xiangguang; Xing, Xiangwei; Zou, Huanxin. 2017. "Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder." Sensors 17, no. 1: 192.
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