Name
Abstract | ||
|---|---|---|
In recent studies, synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms have achieved high recognition accuracy in the moving and stationary target acquisition and recognition (MSTAR) data set. However, these algorithms usually require hundreds or more training samples of each target type. In order to extract azimuth-insensitive features in a SAR ATR task with only a few training samples, a convolutional bidirectional long short-term memory (Conv-BiLSTM) network is designed as an embedding network to map the SAR images into a new feature space where the classification problem becomes easier. Based on the embedding network, a novel few-shot SAR ATR framework called Conv-BiLSTM Prototypical Network (CBLPN) is proposed. Experimental results on the MSTAR benchmark data set have illustrated that the proposed method performs well in SAR image classification with only a few training samples. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2021.03.037 | Neurocomputing |
Keywords | DocType | Volume |
Automatic target recognition (ATR),Deep learning,Synthetic aperture radar (SAR),Few-shot learning | Journal | 443 |
ISSN | Citations | PageRank |
0925-2312 | 1 | 0.36 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Wang | 1 | 250 | 56.88 |
| Xueru Bai | 2 | 1 | 0.36 |
| Ruihang Xue | 3 | 5 | 1.79 |
| Feng Zhou | 4 | 28 | 9.72 |