SAR-ATR

Implementation of "EM Simulation-Aided Zero-Shot Learning for SAR Automatic Target Recognition".

System requirements

• Windows 7
• Python 3.6+/2.7+
• Tensorflow
• SciPy
• NumPy
• MATLAB

Datasets

Two synthetic image are used in this repo. MSTAR data can be downloaded from https://www.sdms.afrl.af.mil/-datasets/mstar/.

• Synthetic image: HB06165_with_mstar.mat
• Synthetic image: HB06181_with_mstar.mat
• Detection network: download it at here and store it in './data/'.
• VGG19 model: download it at here and store it in './data/'.
• Classification model: download it at here and store it in './checkpoint/ZSL/'.

Target detection

Use target_detection to detect the targets in the synthetic image. It read in HB06165_with_mstar.mat, detect the targets, modify the cropped images and save it to ./data/scene_test_181_fans.mat.

Target classification in Python

Run Python main.py --is_test=True to classify the detected images. The classification result wil be saved at ./result/pred.mat.

Show the classification results

Use target_cla to compare the ground truth and recognition results.

Others

• To train the network: Python main.py --mode='ZSL'
• Ablation experiments: Python main.py --mode='-FANS' Python main.py --mode='-Style' Python main.py --mode='-Segmentation'

Authors

• Qian Song Contact me at songq15@fudan.edu.cn.
• Qian Guo
• Wei Ao

Reference

1. Q. Song, H. Chen, F. Xu, and T.J. Cui, "EM Simulation-Aided Zero-Shot Learning for SAR Automatic Target Recognition," IEEE GRSL, 2019.
2. D. Cozzolino, S. Parrilli, G. Scarpa, G. Poggi and L. Verdoliva. “Fast Adaptive Nonlocal SAR Despeckling,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 2, pp. 524-528, 2014.
3. K. Simonyan, and A. Zisserman, “Very Deep Convolutional Networks for Large-scale Image Recognition,” arXiv:1409.1556, 2014.
4. L. J. P. van der Maaten and G. E. Hinton, “Visualizing High-Dimensional Data Using t-SNE,” Journal of Machine Learning Research, vol. 9, pp. 2579-2605, 2008.