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Sequential ISAR Target Classification Based on Hybrid Transformer
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AI
DOI：10.1109/TGRS.2022.3155246.png)
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摘要
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To make full use of the sequential information obtained by continuous inverse synthetic aperture radar (ISAR) imaging, this article proposes a sequential ISAR target classification network based on hybrid transformer (HT). First, a temporal-spatial encoder based on the attention mechanism is designed to extract long-term and global features from sequential images. Meanwhile, a local feature encoder based on the 3-D convolution neural network is designed to extract short-term and local features. Then, the above two features are fused and the classification labels are obtained by a channel encoder-decoder. In 4-satellite target classification experiments, the proposed HT shows high accuracy and robustness to the unknown image scaling, rotation, and combined deformations.
Keyword:
Feature extraction
Transformers
Strain
Radar imaging
Scattering
Training
Trajectory
Attention mechanism
deep learning
inverse synthetic aperture radar (ISAR)
target classification
transformer
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
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引用论文
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Sequence SAR Image Classification Based on Bidirectional Convolution-Recurrent Network基于双向卷积递归网络的序列SAR图像分类
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