CorrNet

This project provides the code and results for 'Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation', IEEE TGRS, vol. 60, pp. 1-12, 2022. IEEE link and arxiv link

Network Architecture

Accuracy v.s. Parameters

Requirements

python 2.7 + pytorch 0.4.0 or

python 3.7 + pytorch 1.9.0

Saliency maps

We provide saliency maps and measure results (.mat) (code: m1dm) of all compared methods (code: kftm) and our CorrNet (code: fbee) on ORSSD and EORSSD datasets.

In addition, we also provide saliency maps of our CorrNet (code: lm21) on the recently published ORSI-4199 dataset.

Training

Modify paths of VGG backbone (code: ego5) in /model/vgg.py and datasets, then run train_CorrNet.py.

Pre-trained model and testing

Download the following pre-trained model, and modify paths of pre-trained model and datasets, then run test_CorrNet.py.

We also uploaded these pre-trained models in /models.

ORSSD (code: vqi7)

EORSSD (code: q5mr)

ORSI-4199 (code: va3b)

Evaluation Tool

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

ORSI-SOD_Summary

Citation

    @ARTICLE{Li_2022_CorrNet,
            author = {Gongyang Li and Zhi Liu and Zhen Bai and Weisi Lin and Haibin Ling},
            title = {Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation},
            journal = {IEEE Transactions on Geoscience and Remote Sensing},
            volume = {60},
            pages = {1-12},
            year = {2022},
            }

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.