## 1 Introduction  
Cloud occlusion due to environmental factors has limited the performance of optical sensors, including domestic optical sensors. Therefore, it is of great significance to accurately extract cloud regions. Accurate extraction of cloud areas brings the following two benefits: First, low-quality images can be pre-checked under high traffic conditions, thereby reducing the amount of downlink data and improving image transmission efficiency; second, accurate extraction of cloud areas helps for land identification, and it provides the basis for subsequent cloud removal and reconstruction. Therefore, the cloud extraction task holds significant importance in image interpretation and quality inspection. Nonetheless, there are still some difficulties in the aspect of cloud detection. They are summarized as follows: (1) **Cloud diversity and ambiguous boundaries:** the significant variations in cloud types and shapes, ranging from thin clouds to thick clouds, pose inherent challenges in cloud extraction tasks. These differences within the cloud class directly lead to substantial intra-class variability. Moreover, the imaging mechanisms associated with the portrayal of thinly cloud-covered regions result in unclear boundaries in the image, further complicating the interpretation process. The lack of distinct edges in thinly cloud-covered regions makes it difficult to discern and interpret these areas accurately, as shown in Figure 1a. (2) **Confounding of terrain and clouds in complex scenes:** depending on the influence of season and climate, local terrain is covered with snow, which reflects the same information from the image as clouds. Therefore, cloud snow against a complex background is difficult to distinguish, as shown in Figure 1b. At present, cloud detection and segmentation approaches are mainly bifurcated into two parts: threshold-based approaches and learning-based approaches [1; 2; 3; 4]. The first type of algorithm is mainly based on cloud spectral characteristics (portions of the electromagnetic spectrum), brightness, texture characteristics and geometry by analyzing the spectral difference between the cloud and other surfaces, thresholds or rules are formulated to realize cloud extraction [5; 6; 7; 8]. [PERSON] et al. [9] employed a dynamic threshold hybrid multi-spectral feature (HMF) method for cloud extraction, which combines three kinds of spectra of normalized difference vegetation index (NDVI), whiteness and haze optimization transform (HOT) features to detect cloud pixels. One can utilize hybrid multi-spectral features. The pure threshold algorithm is straightforward, efficient and applicable for cloud detection; however, the method's impracticality stems from its sensitivity to both background variations and cloud coverage. To enhance the capability to recognize edge details, some people have proposed methods based on machine learning, such as using Support Vector Machine (SVM), and Random Forest (RF) to extract hyper-spectral images [10]. [PERSON] et al. [11] used simple linear iterative clustering (SLIC) to divide optical satellite images into super-pixels, and then calculated the energy and spectral features using Gabor transformation by extracting the texture features. The characteristics of cloud super-pixels serve as the training samples for the SVM classifier. The SVM classifier is trained to establish the classification model. In addition, [PERSON] et al. [12] proposed a multi-dimensional and multi-granularity dense cascade forest (MDForest) for multi-spectral cloud detection. MDForest is a deep forest architecture that introduces a multi-dimensional and multi-granularity scanning mechanism, which enhances the cascade forest representation learning ability. At the same time, the spectral information of the multi-spectral satellite image was captured for cloud extraction. However, its recognition ability is not ideal, especially in complex backgrounds.