## 5 Discussion  
### Advantages  
To fill the current research gap in the field of shade houses extraction, this paper proposes a high-precision shade houses extraction method and verifies its feasibility. The accuracy of deep learning largely depends on the quality of the dataset, and there is currently no specific dataset targeting shade houses. Therefore, this study efficiently creates a high-quality, high-resolution shade house dataset by leveraging the advantages of Segment Anything.  
The proposed ShadeNet is an innovative method for shade house extraction based on self-attention mechanisms and enhanced feature learning, featuring an advanced attention mechanism architecture. As a deep learning-based approach, ShadeNet offers superior portability and feature extraction capabilities compared to traditional remote sensing methods. For instance, spectral index methods are often plagued by poor portability and require solid expertise. While spectral index extraction can achieve good results in small-scale shade houses extraction, it may not be suitable for large-scale extractions, especially with seasonal variations and differences in shade houses covering materials across different regions. Object-based classification methods, due to their high computational cost and limited capability to extract only shallow features, are not ideal for large-scale applications, requiring considerable processing time. In contrast, deep learning methods can explore deeper, more complex features and benefit from efficient parallel computation on GPU devices. Compared to traditional or state-of-the-art network models, ShadeNet's novel architecture design enables it to learn semantic features of various scales and granularities more effectively, thus achieving higher segmentation accuracy when handling complex scenarios.  
Specifically, Transformer-based structures have been tried in the remote sensing image processing field for extracting complex features, especially for large-scale images, where Transformer's self-attention mechanism is effective in capturing long-range dependencies. However, research specifically addressing shade houses extraction remains in the exploratory phase. For example, Swin Transformer [17] has demonstrated strong capabilities in feature extraction at the image level, especially in capturing image details and global context. Nevertheless, it faces challenges in further improving extraction precision. To address this, we propose an improvement combining the Global-Channel and Local-Spatial Attention (GCLSA) module. This enhancement effectively strengthens the model's learning of contextual information and reduces misclassification rates. Considering that Swin Transformer's local window attention mechanism can process high-resolution images, but may have limitations in multi-scale feature fusion and fine-grained information processing, the GCLSA module provides an effective means of enhancement. Through the GCLSA module, the network can dynamically adjust the attention weights of different spatial locations and channels, thus improving the precise extraction of shade house regions. Specifically, the spatial attention mechanism helps the model focus on critical locations in the target area, while the channel attention further strengthens the selective focus on different feature channels, ensuring multi-level information fusion from local to global. This multidimensional attention mechanism enables ShadeNet to better recognize subtle differences between shade houses and their surrounding environment, improving extraction accuracy by capturing both global appearance features and learning deeper contextual information.