## 1 Introduction
[26] innovatively combined the UNet backbone network with a customized Conditional Random Field (CRF) to achieve precise edge segmentation of clouds. [PERSON] et al. [27] introduced a network, CSDUNet, which employs multi-scale feature map concatenation and hierarchical feature extraction to achieve precise pixel-level semantic segmentation of clouds in optical images. [PERSON] et al. [28] proposed MRFA-Net, innovatively designing AFEM and a Multi-scale Attention Mechanism to holistically model both localized patterns and global contextual relationships, effectively addressing multi-scale representation learning challenges in cloud analysis, thereby improving the segmentation accuracy of clouds boundary details. [PERSON] et al. [29] introduced a Selective Kernel (SK) module to capture cloud features of different sizes and designed a Parallel Dilated Convolution Model (PDCM) to enhance the segmentation accuracy of small cloud targets. Additionally, they adopted Content-Aware Reassembly of Features (CARAFE) in the decoder to replace traditional interpolation-based upsampling, effectively restoring fine-grained semantic features. [PERSON] et al. [30] innovatively integrate the precision of state-space modeling for cloud feature characterization with the computational efficiency of encoder-decoder architectures in feature extraction, providing a new solution for cloud detection. [PERSON] et al. [31] designed Tiny U-shaped Blocks (TUBs) and a Simple Feature Fusion Module (SFFM) to enrich spatial contextual information and optimize semantic features in the encoder. Meanwhile, they employed a lightweight Asymmetric Decoder (ALD)to achieve efficient spatial restoration, significantly achieving concurrent optimization of computational throughput and classification fidelity in cloud detection systems. In recent years, following the tremendous success of Transformer models in NLP, researchers have begun applying them to computer vision tasks, including semantic segmentation. The Transformer model was initially proposed by [PERSON] [32] and his colleagues. Its core mechanism, self-attention, empowers the model to not only process sequential data but also capture global information comprehensively. Moreover, it enables the model to effectively model long-range dependencies, which is a significant advantage in dealing with complex data patterns. Compared to CNN-based semantic segmentation methods, Transformers can more effectively capture global contextual information, addressing CNN's limitations in handling long-distance dependencies. Against this backdrop, Vision Transformer (ViT) [33] was proposed in 2020; it signified the inaugural application of the Transformer model to computer vision tasks. The central concept of ViT lies in splitting the input image into small patches and transforming these patches into a sequential input that is suitable for the Transformer architecture, enabling global feature modeling. The remarkable success of the Vision Transformer (ViT) has not only established a robust groundwork for the utilization of Transformer models in semantic segmentation tasks but has also ignited the development of a series of innovative segmentation models. This achievement has opened up new possibilities and directions for the field, demonstrating the great potential of Transformer-based architectures in handling complex visual information related to semantic segmentation. Subsequently, models such as TransUNet [34], SegFormer [35], and Swin Transformer [36] were introduced. These methods combine the global modeling capability of Transformers with the local feature extraction advantages of CNNs, significant enhancements have been attained in terms of both the precision of segmentation and the efficiency of computation. [PERSON] et al. [37] proposed a lightweight prior knowledge-guided Transformer model (LPGT), innovatively designing a Prior Knowledge Extraction module (PKE) and a Prior-guided Efficient Attention Block (PEAB).