## 1 Introduction  
The remarkable progress in scientific and technological domains has propelled revolutionary transformations in remote sensing imaging technologies. Modern remote sensing satellites not only offer higher spatial resolution but also provide high-quality remote sensing imagery across a broader geographical range, delivering unprecedented data support for global environmental monitoring, resource management, and disaster warning applications. This technological progress has significantly expanded the scope of remote sensing research, driving its in-depth development in various application scenarios, such as meteorological disaster monitoring [1], land cover change analysis [2], and natural resource dynamic assessment [3]. Notwithstanding these advancements, the capture and computational analysis of remotely sensed data present persistent challenges that warrant further investigation. Clouds and cloud shadows frequently act as common occlusion factors, often becoming major sources of interference that affect data quality. The presence of clouds may lead to partial or complete loss of ground object information, while cloud shadows can distort ground feature characteristics, resulting in misclassification or information confusion. Such disturbances diminish not only the operational value of remote sensing data products, but also introduce cascading uncertainties that undermine analytical rigor and compromise evidence-based decision-making processes. Specifically, clouds affect the spectral characteristics, morphological structures, and texture information of surface targets to varying degrees, posing significant challenges for high-precision remote sensing data interpretation. Therefore, effectively detecting, segmenting, and restoring cloud and cloud shadow regions has become a critical research topic in remote sensing image processing. In response to this issue, the scientific community has devised multiple solutions, principally divided into classical cloud and shadow detection algorithms and contemporary deep learning-driven techniques. Conventional cloud detection methodologies are typically classified into three primary categories: statistical methods, spectral threshold-based methods, and morphology, as well as texture-based methods. First, statistical methods primarily include statistical equation methods and clustering analysis methods. The statistical equation method constructs mathematical models using brightness, reflectance, and other feature parameters in remote sensing images, detecting clouds by applying predefined computational formulas [4], and further, cloud shadows are matched to their corresponding clouds based on geometric relationships between them [5; 6; 7; 8; 9; 10]. Meanwhile, the clustering-based approach segments remote sensing image pixels into discrete categories by jointly analyzing their spectral attributes and spatial arrangement characteristics, thereby identifying and separating cloud regions [11]. These methods are advantageous due to their relatively intuitive mathematical models and suitability for structured data. However, in complex scenarios--particularly when cloud morphology varies significantly or mixed pixels are present--the stability and accuracy of detection may be affected. Spectral threshold-based methods utilize the reflectance differences in clouds across different spectral bands for detection. This analytical framework permits bifurcation into either unimodal temporal scene examination or sequential temporal image assessment paradigms. Single-period image analysis relies on remote sensing data captured at a specific moment, using predefined spectral thresholds in selected bands to distinguish between clouds and surface objects [12; 13]. In contrast, multi-temporal image analysis leverages temporal changes in the spectral characteristics of clouds and cloud shadows across different time points for detection [14; 15; 16]. Spectral threshold methods offer high computational efficiency and are well-suited for large-scale automated processing.