QianSong-Cherry/SAR-ZSL_for_featureSpace

Code for paper "Zero-Shot Learning of SAR Target Feature Space With Deep Generative Neural Networks"

SAR-ZSL_for_featureSpace

Code for paper "Zero-Shot Learning of SAR Target Feature Space With Deep Generative Neural Networks"

Overall framework of the generative DNN-based SAR target feature space construction and interpretation:

Constructor–generator network for MSTAR data:

Inverse interpreter DNN for MSTAR data:

Prerequisites

• Python 2.7
• Tensorflow
• Scipy
• Numpy

Useage

First download the data and save as .mat format

to pre-train the constructor NN: $ python preTrain.py

to train constructor–generator network: $ python train.py

to train or inferece interpreter DNN: $ python classification.py

Results

Generated full-aspect SAR images:

Authors

• Qian Song
• Feng Xu

References

[1] Q. Song and F. Xu, "Zero-Shot Learning of SAR Target Feature Space With Deep Generative Neural Networks," IEEE GRSL, vol. 14, no. 12, 2017.

[2] A. Dosovitskiy et al., "Learning to Generate Chairs, Tables and Cars with Convolutional Networks," arXiv:1411.5928v3.