SeaNet

This project provides the code and results for 'Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment', IEEE TGRS, 2023. IEEE link and arxiv link Homepage

Network Architecture

Requirements

python 3.7 + pytorch 1.9.0

Saliency maps

We provide saliency maps of our SeaNet on ORSSD, EORSSD, and additional ORSI-4199 datasets in './models/saliency_maps.zip'.

Training

We use data_aug.m for data augmentation.

Modify paths of datasets, then run train_SeaNet.py.

Note: our main model is under './model/SeaNet_models.py'

Pre-trained model and testing

We provide the pre-trained models in './models/'.
Modify paths of pre-trained models and datasets.
Run test_SeaNet.py.

Evaluation Tool

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

ORSI-SOD_Summary

Citation

    @ARTICLE{Li_2023_SeaNet,
            author = {Gongyang Li and Zhi Liu and Xinpeng Zhang and Weisi Lin},
            title = {Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment},
            journal = {IEEE Transactions on Geoscience and Remote Sensing},
            volume = {61},
            year = {2023},
            doi = {10.1109/TGRS.2023.3235717},
            }

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.

MathLee/SeaNet