## 1 Introduction
In addition, the overall accuracy of the above cloud detection methods also is up to the number of image bands to effectively extract clouds. Recently, convolutional neural network (CNN) methods have also been used to detect clouds. For example, the M-type convolutional network model RM-Net uses atrous spatial pyramid pooling (ASPP). ASPP consists of atrous convolution and pyramid pooling [13]. When scaling features, the phenomenon that the information loss caused by multiple downsampling is effectively reduced. It extracts multi-scale features of images without losing information and combines residual units to make the network less prone to degradation. The encoding and decoding modules extract the global context information of the image, judge the class probability of each pixel according to the fused features, and input it into the classifier for pixel-level cloud and non-cloud segmentation [14]. With the advancement of artificial intelligence, deep learning algorithms exhibit remarkable performance in image interpretation, particularly in the domain of optical satellite remote sensing imagery. Different from natural images, satellite images from optical sensors have a larger scale, more coverage and richer ground truth details. From the perspective  
Figure 1: Confused cloud detection instance (red rectangles). (**a**) Boundaries of thin clouds are not clear; (**b**) Clouds and snow are confused. of deep learning, we categorize cloud extraction methods into the following two classes: CNN-based and CNN-Trans-based methods. (a) CNN-based: U-Net [15] is a classic image segmentation method with excellent performance in many binary classification tasks. Numerous studies have demonstrated that methods of the U-shape structure [16; 17] showcase outstanding performance in the segmentation of optical satellite images [18]. As an illustration, CloudU-Net, which is a structure derived from U-Net, uses Atrous Convolution instead of the traditional convolution layer to enlarge the field of view. It increases the training speed through batch normalization, which can also prevent over-fitting of the model [19]. Although the algorithm performs well in the case of dense small objects, it does not take into account the diversity of clouds, because the characteristics of clouds are uncertain. (b) CNN-Trans-based: Self-attention mechanism is a core part of the transformer algorithm. The attention mechanism draws inspiration from human visual cognition science, where individuals naturally concentrate on detailed information related to a target while suppressing irrelevant details when reading text or observing objects. It is a process that goes from coarse to fine. The integration of the attention mechanism into CNN networks was introduced by researchers in 2017 [20]. Since then, attention-based mechanisms in CNNs have found widespread application across various domains. The CNN-Trans attention module comprises two key components: the channel and spatial attention modules, respectively. The former accentuates the correlation among the dimensions of each layer, which forces the attention on the interested feature information and suppresses the useless channel. The latter can retain high-frequency feature information through spatial operation. On one hand, [PERSON] et al. [21] introduced a novel CNN unit called the squeeze-and-excitation block, which dynamically adjusts the feature response value by modeling inter-channel relationships. In comparison, CBAM [22] not only incorporates spatial attention but also employs a concurrent structure involving multiscale pyramid pooling within the channel attention. Experimental validation attests to its effectiveness. Spatial attention, on the other hand, directs focus to regions of interest in the spatial aspect. While the attention mechanism has demonstrated considerable utility since its integration into CNNs, the issue of redundancy remains a common challenge.