# CloudformerV3: Multi-Scale Adapter and Multi-Level Large Window Attention for Cloud Detection
## 2 Related Work  
The traditional cloud detection method is based on spectral thresholds, which utilize the unique physical characteristics of clouds to construct multiple spectral thresholds. Among them, Automated Cloud Cover Assessment [9; 10] (ACCA) and Function of Mask (Fmask) [11] are the most representative methods. However, methods based on spectral thresholds typically require remote sensing images to possess rich spectral information. Therefore, these methods are often applied to imagery from the Landsat series or Sentinel-2 satellites, which provide extensive spectral data. For remote sensing images with a limited number of spectral bands or those consisting primarily of visible light, such algorithms may face challenges in effective operation. Moreover, methods based on spectral thresholds often exhibit poor generalization capabilities, with limited robustness, particularly in diverse and complex scenes [12; 13]. Modern cloud detection technology is usually based on classic machine learning methods, using algorithms such as Support Vector Machine (SVM) [14] and Random Forest (RF) [15] to identify the cloud. The prediction time complexity for SVM is generally \(O(d)\), where \(d\) is the number of features. And the prediction time complexity for each tree is typically \(O(log(n))\), where \(n\) is the number of nodes in the tree. These methods make better use of spatial information from remotely sensed images and also reduce the high dependence of remotely sensed image data on spectral range. However, due to the fact that classical machine learning models typically require manual design of image features,this method is difficult to effectively extract higher-level semantic information and has poor robustness to complex scenes [13]. In recent years, deep neural networks have shown good performance in segmentation tasks due to their powerful feature extraction capabilities. This method has also been applied to remote sensing image cloud detection and achieved good results. Inspired by convolutional neural networks for semantic segmentation such as FCN [16], SegNet [17], UNet [18], and DeepLabV3+ [19], [PERSON] et al. proposed CloudFCN [20], a method of cloud detection in combination with Inception module, while [PERSON] et al. proposed RSNet (Remote Sensing Network) [21] for remote sensing images in RGB. As researchers delve deeper into the realm of cloud detection tasks, they have recognized that devising effective methods tailored to the distinct characteristics and challenges of cloud detection tasks stands as a pivotal technology for enhancing algorithm precision. [PERSON] and colleagues introduced CDNet [22], a solution designed for low-resolution remote sensing thumbnail images. This innovation bolsters the accuracy of detecting clouds in low-resolution images through edge refinement and the integration of a feature pyramid structure. [PERSON] et al. focused their efforts on high-resolution remote sensing images and devised MSCFF [23], which employs a multi-scale feature fusion approach. [PERSON] and his team proposed CDNetV2 [24], a model that attains high-precision cloud detection, even in scenarios where clouds and snow coexist. In 2022, [PERSON] et al. proposed a lightweight network, DABNet [25], which creates not only a lower false-alarm rate but also a clearer boundary. In the same year, to effectively detect thin clouds, [PERSON] et al. proposed a novel robust cloud detection approach, GCDB-UNet [26], which embeds GCDB (global context dense block) to UNet. The time complexity of convolutional neural network models generally depends on the structure of the model. When the number of input pixels is \(n\), the time complexity of one convolutional operation is usually \(O\left(n\times K^{2}\times C\right)\), where \(K\) is the size of the convolutional kernel and \(C\) is the number of output channels. Typically, convolutional neural networks have higher time complexity compared to traditional models.