Semantic Scholar Open Access 2022 72 sitasi

Convolutional Neural Network With Attention Mechanism for SAR Automatic Target Recognition

Ming Zhang Jubai An D. Yu L. Yang Liang Wu +1 lainnya

Abstrak

Synthetic aperture radar automatic target recognition (SAR ATR) is a key technique of remote-sensing image recognition, which has many potential applications in the fields of military surveillance, national defense, civil application, and so on. With the development of science and technology, deep convolutional neural network (DCNN) has been widely applied for SAR ATR. However, it is difficult to use deep learning to train models with limited ray SAR images. To resolve this problem, we proposed an effectively lightweight attention mechanism CNN (AM-CNN) model for SAR ATR. Extensive experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set illustrate that the AM-CNN model can achieve a superior recognition performance, and the average recognition accuracy can reach 99.35% on the classification of 10 class targets. Compared with the traditional CNN and the state-of-the-art method, our model is significantly superior to improve performance and efficiency.

Topik & Kata Kunci

Computer Science

Penulis (6)

Ming Zhang

Jubai An

D. Yu

L. Yang

Liang Wu

X. Lu

Format Sitasi

APA MLA BibTeX

Zhang, M., An, J., Yu, D., Yang, L., Wu, L., Lu, X. (2022). Convolutional Neural Network With Attention Mechanism for SAR Automatic Target Recognition. https://doi.org/10.1109/LGRS.2020.3031593

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 72×
Sumber Database: Semantic Scholar
DOI: 10.1109/LGRS.2020.3031593
Akses: Open Access ✓