This project provides some SAR specific models which have a strong ability to extract spatial features of single-polarization Synthetic Aperture Radar images.
A novel deep learning framework Deep SAR-Net has been also proposed for SLC SAR images, together with trained models.
[1] Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning, IEEE GRSL 2020, Accepted
doi: 10.1109/LGRS.2020.2965558 url: https://arxiv.org/abs/2001.01425
[2] What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs, IEEE TGRS, Accepted
doi: 10.1109/TGRS.2019.2947634 url: https://arxiv.org/abs/1906.01379
[3] Deep SAR-Net: Learning Objects from Signals, submitted to ISPRS Journal of Photogrammetry and Remote Sensing (undergoing review)
pytorch 0.4.0 python 3.6
./models/resnet18_I_nwpu_cate45_tsx_level1_cate7_col36_imb_ce+topk+.pth
see ref[1]
./models/resnet18_tsx_mstar_epoch7.pth
see ref[1] achieving an overall accuracy of 99.46% on MSTAR 10-class target recognition task without data augmentation
./models/alexnet_tsx.pth
see ref[2]
./models/alexnet_tsx_mstar_iter1920.pth
see ref[2] achieving an overall accuracy of 99.34% on MSTAR 10-class target recognition task
./models/slc_joint_deeper_3_F.pth
see ref[3] training with 90% data