OAMSFNet: Orientation-Aware and Multi-Scale Feature Fusion Network for shadow detection in remote sensing images via pseudo shadow

ABSTRACT

Remote sensing images (RSI) of urban regions often exhibit shadows cast by buildings and other objects, which may result in imprecise analysis and interpretation. Therefore, shadow detection plays a significant role in RSI scene understanding. Current approaches have paid little attention to the presence of pseudo shadows and their confounding effects on detection results. We tackle these questions in the spatial context of orientation-awareness, effectively capturing the intricate relationships between shadows and ground objects. Here, we introduce a novel deep learning network named the Orientation-Aware and Multi-Scale Feature Fusion Network (OAMSFNet) due to noise reduction considerations. The proposed OAMSFNet comprises Shadow Aware Feature Encoder (SFE), Orientation-Aware Context Module (OCM), and Multi-Scale Feature Pyramid (MFP). Furthermore, a multi-scale feature fusion algorithm has been devised to enhance the detection and segmentation capabilities of the model in shadow regions. Finally, a comparative study was conducted on the Aerial Imagery dataset for Shadow Detection (AISD) in both quantitative and qualitative aspects. The experimental results show that our method outperforms state-of-the-art methods while maintaining a lightweight model design, indicating that our approach exhibits remarkable accuracy and stability in excellent agreement with predictions. OAMSFNet achieved an average F-score of 89.85%, surpassing the straightforward semantic segmentation model SegNet by 9.94%, and demonstrated remarkable efficiency enhancements, with FLOPs reduced to as low as 11.48 G, resulting in efficiency improvements of 66.7%-96.5% compared to other shadow detection models.

KEYWORDS:

• Shadow detection
• pseudo shadow
• deep learning
• remote sensing images
• spatial context

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The Aerial Imagery dataset for Shadow Detection used for training and testing is available at https://github.com/RSrscoder/AISD.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Grant number 41971365), the Major Science and Technology Project of the Ministry of Water Resources (Grant number SKR-2022037), the Chongqing Higher Education Teaching Reform Research Project (Grant number 201019S), and the Chongqing Graduate Research Innovation Project (Grant number CYS22448)