ShadeNet: Innovating Shade House Detection via High-Resolution Remote Sensing and Semantic Segmentation
Source abstract
Abstract
Shade houses are critical for modern agriculture, providing optimal growing conditions for shade-sensitive crops. However, their rapid expansion poses ecological challenges, making the accurate extraction of their spatial distribution crucial for sustainable development. The unique dark appearance of shade houses leads to low accuracy and high misclassification rates in traditional spectral index-based extraction methods, while deep learning approaches face challenges such as insufficient datasets, limited receptive fields, and poor generalization capabilities. To address these challenges, we propose ShadeNet, a novel method for shade house detection using high-resolution remote sensing imagery and semantic segmentation. ShadeNet integrates the Swin Transformer and Mask2Former frameworks, enhanced by a Global-Channel and Local-Spatial Attention (GCLSA) module. This architecture significantly improves multi-scale feature extraction and global feature capture, thereby enhancing extraction accuracy. Tested on a self-labeled dataset, ShadeNet achieved a mean Intersection over Union (mIOU) improvement of 2.75% to 7.37% compared to existing methods, significantly reducing misclassification. The integration of the GCLSA module within the Swin Transformer framework enhances the model’s ability to capture both global and local features, addressing the limitations of traditional CNNs. This innovation provides a robust solution for shade houses detection, supporting sustainable agricultural development and environmental protection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Automated Road and Building Extraction · Remote Sensing and LiDAR Applications · Human Mobility and Location-Based Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen Campus, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China
Funding
National Key R&D Program of Chin
References (47)
Show source-provided references
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