Semi-supervised SAR Ship Target Detection with Graph Attention Network

LÜ Jindong; WANG Tong; TANG Xiaobin

doi:10.11999/JEIT220139

Volume 45 Issue 5

May 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(5): 1541-1549

LÜ Jindong, WANG Tong, TANG Xiaobin. Semi-supervised SAR Ship Target Detection with Graph Attention Network[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1541-1549. doi: 10.11999/JEIT220139

Citation:

LÜ Jindong, WANG Tong, TANG Xiaobin. Semi-supervised SAR Ship Target Detection with Graph Attention Network[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1541-1549. doi: 10.11999/JEIT220139

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Semi-supervised SAR Ship Target Detection with Graph Attention Network

doi: 10.11999/JEIT220139

1.
National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
Academy of Electronics and Information Technology, China Electronic Technology Group Corporation, Beijing 100041, China

Funds: The National Key R&D Program of China (2016YFE0200400)

Received Date: 2022-02-15
Rev Recd Date: 2022-06-21

Available Online: 2022-06-30

Publish Date: 2023-05-10

Abstract

Abstract

Recently, ship target detection in Synthetic Aperture Radar (SAR) imagery based on deep learning has been widely developed. However, a large number of labeled samples are needed in traditionally supervised learning to train the network. Therefore, a semi-supervised SAR ship target detection approach based on Graph ATtention network (GAT) is proposed. Firstly, a symmetric convolutional neural network is designed to realize land-ocean segmentation. Secondly, the super-pixel segmentation is completed and the super-pixels are modeled as nodes of the GAT. The multi-scale features of a node are extracted by region of interest pooling layer. Attentional mechanisms are used in GAT to concatenate adaptively the neighbor node’s features and classify the unlabeled nodes. Finally, the super-pixels predicted as ship targets are located in SAR image and the fine detection results are obtained. The proposed method is verified on the measured high resolution SAR images dataset. The results show that this method can effectively detect ship targets with low false alarm rate by using a small number of labeled samples.
- Radar target detection,
- Graph ATtention (GAT) network,
- Semi-supervised learning,
- Ship target detection

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References(18)

References

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