Awesome

Global Semantic-Sense Aggregation Network for Salient Object Detection in Remote Sensing Images

⭐ This code has been completely released ⭐

⭐ our article ⭐

📖 Introduction

<span style="font-size: 125%">Salient object detection (SOD) aims to accurately identify significant geographical objects in remote sensing images (RSI), providing reliable support and guidance for extensive geographical information analyses and decisions. However, SOD in RSI faces numerous challenges, including shadow interference, inter-class feature confusion, as well as unclear target edge contours. Therefore, we designed an effective Global Semantic-aware Aggregation Network (GSANet) to aggregate salient information in RSI. GSANet computes the information entropy of different regions, prioritizing areas with high information entropy as potential target regions, thereby achieving precise localization and semantic understanding of salient objects in remote sensing imagery. Specifically, we proposed a Semantic Detail Embedding Module (SDEM), which explores the potential connections among multi-level features, adaptively fusing shallow texture details with deep semantic features, efficiently aggregating the information entropy of salient regions, enhancing information content of salient targets. Additionally, we proposed a Semantic Perception Fusion Module (SPFM) to analyze map relationships between contextual information and local details, enhancing the perceptual capability for salient objects while suppressing irrelevant information entropy, thereby addressing the semantic dilution issue of salient objects during the up-sampling process. The experimental results on two publicly available datasets, ORSSD and EORSSD, demonstrated the outstanding performance of our method. The method achieved 93.91% Sα, 98.36% Eξ, and 89.37% Fβ on the EORSSD dataset.</span>

If our code is helpful to you, please cite:

@Article{e26060445,
AUTHOR = {Li, Hongli and Chen, Xuhui and Yang, Wei and Huang, Jian and Sun, Kaimin and Wang, Ying and Huang, Andong and Mei, Liye},
TITLE = {Global Semantic-Sense Aggregation Network for Salient Object Detection in Remote Sensing Images},
JOURNAL = {Entropy},
VOLUME = {26},
YEAR = {2024},
NUMBER = {6},
ARTICLE-NUMBER = {445},
URL = {https://www.mdpi.com/1099-4300/26/6/445},
ISSN = {1099-4300},
ABSTRACT = {Salient object detection (SOD) aims to accurately identify significant geographical objects in remote sensing images (RSI), providing reliable support and guidance for extensive geographical information analyses and decisions. However, SOD in RSI faces numerous challenges, including shadow interference, inter-class feature confusion, as well as unclear target edge contours. Therefore, we designed an effective Global Semantic-aware Aggregation Network (GSANet) to aggregate salient information in RSI. GSANet computes the information entropy of different regions, prioritizing areas with high information entropy as potential target regions, thereby achieving precise localization and semantic understanding of salient objects in remote sensing imagery. Specifically, we proposed a Semantic Detail Embedding Module (SDEM), which explores the potential connections among multi-level features, adaptively fusing shallow texture details with deep semantic features, efficiently aggregating the information entropy of salient regions, enhancing information content of salient targets. Additionally, we proposed a Semantic Perception Fusion Module (SPFM) to analyze map relationships between contextual information and local details, enhancing the perceptual capability for salient objects while suppressing irrelevant information entropy, thereby addressing the semantic dilution issue of salient objects during the up-sampling process. The experimental results on two publicly available datasets, ORSSD and EORSSD, demonstrated the outstanding performance of our method. The method achieved 93.91% Sα, 98.36% Eξ, and 89.37% Fβ on the EORSSD dataset.},
DOI = {10.3390/e26060445}
}

Saliency maps

We provide saliency maps of our and compared methods at here (code:o4dz) on two datasets (ORSSD and EORSSD).

DateSets

ORSSD download at here

EORSSD download at here

The structure of the dataset is as follows:

GSANet
├── EORSSD
│   ├── train
│   │   ├── images
│   │   │   ├── 0001.jpg
│   │   │   ├── 0002.jpg
│   │   │   ├── .....
│   │   ├── lables
│   │   │   ├── 0001.png
│   │   │   ├── 0002.png
│   │   │   ├── .....
│   │   
│   ├── test
│   │   ├── images
│   │   │   ├── 0004.jpg
│   │   │   ├── 0005.jpg
│   │   │   ├── .....
│   │   ├── lables
│   │   │   ├── 0004.png
│   │   │   ├── 0005.png
│   │   │   ├── .....

Train

1. Download the dataset.

2. Use data_aug.m to augment the training set of the dataset.

3. Download uniformer_base_ls_in1k.pth (code: o4dz), and put it in './pretrain/'.

4. Modify paths of datasets, then run train_MyNet.py.

Test

1. Download the pre-trained models of our network at here (code:o4dz)
2. Modify paths of pre-trained models and datasets.
3. Run test_MyNet.py.

Results

Main results on ORSSD dataset

• Bold indicates the best performance.

Main results on EORSSD dataset

• Bold indicates the best performance.

Visualization of results

Evaluation Tool

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

ORSI-SOD Summary

Salient Object Detection in Optical Remote Sensing Images Read List at here

Acknowledgements

This code is built on PyTorch.

Contact

If you have any questions, please submit an issue on GitHub or contact me by email (cxh1638843923@gmail.com).