This project is for paper "Uncertainty Exploration: Toward Explainable SAR Target Detection"
If you found this code useful, please cite our paper
@ARTICLE{huang2023,
author={Huang, Zhongling and Liu, Ying and Yao, Xiwen and Ren, Jun and Han, Junwei},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Uncertainty Exploration: Toward Explainable SAR Target Detection},
year={2023},
volume={61},
pages={1-14},
doi={10.1109/TGRS.2023.3247898}}- Bayesian deep detectors (BDDs) for horizontal and oriented SAR targets are constructed for uncertainty quantification, answering how much to trust the classification and localization result.
- An occlusion-based explanation method (U-RISE) for BDD is proposed to account for the SAR scattering features that cause uncertainty or promote trustworthiness.
- Counterfactual analysis is conducted to verify the cause-and-effect relationship between the U-RISE explanation and BDD prediction.
Code is based on an oriented object detection toolbox, OBBdetection. Please refer to install.md of OBBdetection for installation and dataset preparation.
- Train for HBB
python tools/train.py uncertainty/config/HBB/fcos_r50_caffe_fpn_gn-head_4x4_SSDD_MCdropout.py- Train for OBB
python tools/train.py uncertainty/config/OBB/fcos_obb_r50_caffe_fpn_gn-head_4x4_SSDD+_MCdropout.py- Get the probabilistic detection results.
python uncertainty/inference/Bayesian_inference.py --img demo/000009.jpg --config ckpt/SSDD+/FCOS_MCdropout/fcos_obb_r50_caffe_fpn_gn-head_4x4_SSDD+_MCdropout.py --checkpoint ckpt/SSDD+/FCOS_MCdropout/epoch_36.pth --out ckpt/SSDD+/FCOS_MCdropout/out --show- Get the explanation result for one interested detected instance.
python uncertainty/explanation/Attribution_Analysis.py --img demo/000009.jpg --config ckpt/SSDD+/FCOS_MCdropout/fcos_obb_r50_caffe_fpn_gn-head_4x4_SSDD+_MCdropout.py --checkpoint ckpt/SSDD+/FCOS_MCdropout/epoch_36.pth --result ckpt/SSDD+/FCOS_MCdropout/out/000009/prob_results.txt