基于散射激励学习和信道压降的SAR图像中的遮挡目标识别.zip

## Occluded Target Recognition in SAR Imagery withScattering Excitation Learning and Channel Dropout # Introduction We propose a novel robust SAR recognition method against occlusion. Specifically, we design a scattering excitation learning module that encourages the network to learn more robust features responding to the scattering centers of targets. In addition, we adopt a random feature channel dropout technique which can further improve robustness to occlusion. Our method makes the network more robust against occlusion but without any occlusion-simulated data for training.  <p align="center">The whole framework of the proposed approach that integrates SEL and channel-wise dropout.</p> # Prerequisites Python 3.7.7 torch == 1.10.0+cu102 torchvision== 0.11.0+cu102 opencv-python == 4.6.0 ***The environment must be strict*** # Getting Started 1.Dataset download + MSTAR can be downloaded [here](https://pan.baidu.com/s/103kb3sg65iSY87gGqadpBA) 提取码：lzad You could download datasets and put them in `./data` folder for train and evaluation. 2.Training ``` python train.py --dataset MSTAR ``` 3.Evaluation + The trained model has been placed in `./chkpt` folder. + You can directly evaluate the MSTAR dataset. ``` python test.py --dataset MSTAR ```