This project provides the code and results for 'Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images', IEEE TCYB, 2023. IEEE link and arxiv link

python 2.7 + pytorch 0.4.0 or

python 3.7 + pytorch 1.9.0

We provide saliency maps of our ACCoNet (VGG_backbone (code: gr06) and ResNet_backbone (code: 1hpn)) on ORSSD, EORSSD, and additional ORSI-4199 datasets.

We provide the code for ACCoNet_VGG and ACCoNet_ResNet, please modify '--is_ResNet' and the paths of datasets in train_ACCoNet.py.

For ACCoNet_VGG, please modify paths of VGG backbone (code: ego5) in /model/vgg.py.

data_aug.m is used for data augmentation.

1. Download the following pre-trained models and put them in /models.

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

3. Run test_ACCoNet.py.

ORSSD: ACCoNet_VGG (code: 1bsg); ACCoNet_ResNet (code: mv91).

EORSSD: ACCoNet_VGG (code: i016); ACCoNet_ResNet (code: ak5m).

ORSI-4199: ACCoNet_VGG (code: qv05); ACCoNet_ResNet (code: art7).

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

    @ARTICLE{Li_2023_ACCoNet,
            author = {Gongyang Li and Zhi Liu and Dan Zeng and Weisi Lin and Haibin Ling},
            title = {Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images},
            journal = {IEEE Transactions on Cybernetics},
            volume={53},
            number={1},
            pages={526-538},
            year={2023},
            month={Jan.},
            }

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

Please contact me at [email protected] or [email protected].