## 2 Related Works
Although spectral index methods can effectively utilize spectral reflectance information from remote sensing imagery, they still face challenges such as \"same spectra for different objects\" and \"different spectra for the same object\", which can result in misclassification and lower extraction accuracy [14]. [PERSON]'s study [13] indicated that shade houses and other types of PCGs exhibit significant spectral differences, while the widespread use of black plastic mulching and black shading nets (for non-agricultural purposes) complicates the accurate identification of shade houses. Therefore, existing spectral index methods for PCG extraction have not considered the unique characteristics of shade houses, indicating that previous studies have largely overlooked this extraction issue. Given the technical difficulties associated with spectral index methods for extracting shade houses, deep learning approaches may be more suitable for this task. Deep learning provides a novel method for automatically generating high-level and more representative features based on multi-layered neural network architectures [26]. With the emergence of efficient and accurate network architectures such as U-Net [27], HRNet [28], and EfficientNet [29], along with the increasing availability of high-resolution remote sensing imagery, the current mainstream methods for extracting PCGs using deep learning are often based on CNNs and high-resolution remote sensing imagery. [PERSON] et al. compared the performance of three classic object detection algorithms for extracting PCGs using mixed imagery from Gaofen-2 and Gaofen-1 satellites, finding that YOLOv3 [30] could quickly and accurately detect PCGs [31]. [PERSON] et al. proposed an improved deep convolutional neural network to accurately distinguish PCGs from PMFs in Google imagery and applied the results for mapping [8]. [PERSON] et al. modified the upsampling step in the U-Net decoder [27] by implementing parallel transpose convolution and bilinear upsampling, which alleviated noise in the extraction results [32]. However, the encoder used a simple stacked \"convolution-activation-pooling\" structure, which limited its ability to extract high-level semantic features for PCGs. [PERSON] et al. proposed a dense object dual-task deep learning (DELTA) framework based on 1 m spatial resolution remote sensing imagery to address large-scale mapping of PCGs [33]. This framework employs a dual-task learning module to simultaneously extract greenhouse area and quantity. In applications across six regions of China, this method achieved a 1.8% improvement in average precision over Faster R-CNN and successfully extracted more than 13 million greenhouses, demonstrating the advantages of deep learning techniques in large-scale automatic extraction. [PERSON] et al. developed an improved U-Net model that successfully extracted shade houses and PCGs through multi-task learning, particularly achieving significant performance improvements in shade house extraction compared to traditional methods [15]. However, the study had some limitations, including a limited number of shade house samples in the dataset, insufficient identification accuracy, and difficulty distinguishing shade houses from similar objects such as roads and building shadows. Furthermore, since the study was primarily conducted in a flat farmland environment, the method showed limited adaptability and generalization capability when applied to more complex backgrounds. Networks for shade house extraction require a richer receptive field to capture both the target's semantic features and contextual information, which is necessary to distinguish easily confused objects. Although several novel and convenient CNN-based methods have been proposed for PCG extraction in the literature, these models inherently have smaller receptive fields due to CNN structure limitations, which hinders their ability to capture global semantic information, especially when analyzing remote sensing images with complex backgrounds [16].