This project provides the code and results for 'Salient Object Detection in Optical Remote Sensing Images Driven by Transformer', IEEE TIP, 2023. IEEE and arxiv Homepage

python 3.8 + pytorch 1.9.0

We provide saliency maps of our GeleNet on three datasets in './GeleNet_saliencymap_PVT.zip' (PVT-v2-b2 backbone) and './GeleNet_saliencymap_SwinT.zip' (Swin Transformer backbone).

We also provide saliency maps of all compared methods (code: 2892) on three datasets.

We use data_aug.m for data augmentation.

Download pvt_v2_b2.pth (code: sxiq), and put it in './model/'.

Modify paths of datasets, then run train_GeleNet.py.

Note: Our main model is under './model/GeleNet_models.py' (PVT-v2-b2 backbone)

1. Download the pre-trained models (PVT-v2-b2 backbone) on ORSSD (code: qga2), EORSSD (code: ahm7), and ORSI-4199 (code: 5h3u), and put them in './models/'.

2. Modify paths of pre-trained models and datasets.

3. Run test_GeleNet.py.

You can use the evaluation tool (MATLAB version) to evaluate the above saliency maps.

    @ARTICLE{Li_2023_GeleNet,
            author = {Gongyang Li and Zhen Bai and Zhi Liu and Xinpeng Zhang and Haibin Ling},
            title = {Salient Object Detection in Optical Remote Sensing Images Driven by Transformer},
            journal = {IEEE Transactions on Image Processing},
            volume = {32},
            pages = {5257-5269},
            year = {2023},
            }

If you encounter any problems with the code, want to report bugs, etc.

Please contact me at lllmiemie@163.com or ligongyang@shu.edu.cn.