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Article

Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism

The National Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China
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Author to whom correspondence should be addressed.
Academic Editors: Jean-Christophe Cexus and Ali Khenchaf
Remote Sens. 2021, 13(14), 2686; https://doi.org/10.3390/rs13142686
Received: 24 May 2021 / Revised: 5 July 2021 / Accepted: 5 July 2021 / Published: 8 July 2021
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods. View Full-Text
Keywords: synthetic aperture radar (SAR); target detection; convolutional neural network (CNN); semi-supervised learning; attention mechanism synthetic aperture radar (SAR); target detection; convolutional neural network (CNN); semi-supervised learning; attention mechanism
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MDPI and ACS Style

Wei, D.; Du, Y.; Du, L.; Li, L. Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism. Remote Sens. 2021, 13, 2686. https://doi.org/10.3390/rs13142686

AMA Style

Wei D, Du Y, Du L, Li L. Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism. Remote Sensing. 2021; 13(14):2686. https://doi.org/10.3390/rs13142686

Chicago/Turabian Style

Wei, Di, Yuang Du, Lan Du, and Lu Li. 2021. "Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism" Remote Sensing 13, no. 14: 2686. https://doi.org/10.3390/rs13142686

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