🆕 The Latest Version of DSN https://github.com/Alien9427/DSN
@article{dsn2020,
title = {Deep SAR-Net: Learning objects from signals},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {161},
pages = {179-193},
year = {2020},
issn = {0924-2716},
author = {Z. Huang and M. Datcu and Z. Pan and B. Lei},
}
🆕 More SAR image pre-trained models are referred to SAR-HUB
This project provides some SAR specific models with strong abilities to extract spatial features of single-polarization Synthetic Aperture Radar (SAR) amplitude images.
Pytorch 0.4.0 (also verified in Pytorch 1.4.0) Python 3.6
./model/resnet18_I_nwpu_tsx.pth [1]
The SAR image pre-trained model in Reference [1].
It can be transferred to other SAR classification or detection models with ResNet-18 backbone.
Use MMDetection to transfer the pretrained model to SAR detection:
import torchvision.models as models
resnet18 = models.resnet18(pretrained=False)
pthfile = './model/resnet18_I_nwpu_tsx.pth'
resnet18.load_state_dict(torch.load(pthfile))
./model/resnet18_tsx_mstar_epoch7.pth [1]
The transferred model to MSTAR 10-class target recognition task in Reference [1], achieving an overall accuracy of 99.46%.
./model/alexnet_tsx.pth [2]
The SAR-image pre-trained model in Reference [2].
./model/alexnet_tsx_mstar_iter1920.pth [2]
The transferred model to MSTAR 10-class target recognition task in Reference [2], achieving an overall accuracy of 99.34%.
[1] Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning, IEEE GRSL 2020
doi: 10.1109/LGRS.2020.2965558
[2] What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs, IEEE TGRS 2019