## 1 Introduction
However, their effectiveness depends heavily on the spectral characteristics of the images, requiring recalibration of thresholds for different sensor-acquired images. Additionally, their accuracy may be limited when dealing with thin clouds, cirrus clouds, or cloud-shadow confusion. Morphology- and texture-based methods detect clouds and cloud shadows by analyzing their geometric shapes, edge features, and texture information [17]. Clouds typically exhibit soft or diffuse edges, whereas cloud shadows often have a geometric correspondence with cloud positions. Therefore, morphological operators and texture analysis techniques (such as gray-level co-occurrence matrices and Gabor filtering) can be used to differentiate between clouds and background surface features. Furthermore, some researchers have proposed hybrid methods that integrate multiple features--combining spectral, morphological, and texture characteristics--to improve cloud detection accuracy [18]. Despite these advancements, traditional methods still face challenges when dealing with different types of clouds. For instance, thin clouds contain both cloud spectral information and underlying surface details, making them difficult to identify using single-feature detection methods. Moreover, cloud shadows can have spectral similarities with certain surface objects (such as water bodies or shaded areas), leading to misclassification. As a result, traditional methods often require additional discriminative rules to eliminate interference when detecting complex clouds or dynamically changing cloud shadows, which in turn increases computational complexity. Although traditional statistical, spectral threshold, and morphological texture-based methods perform well on specific datasets, they still suffer from high computational complexity, strong dependency on specific data parameters, and limited generalization ability. Additionally, many of these methods require manually set empirical parameters, which reduces their adaptability across different datasets. With the rapid advancement of big data and artificial intelligence technologies, machine learning and deep learning methods have been increasingly applied to the automatic detection of clouds. Compared to traditional methods, machine learning algorithms can automatically learn data features, eliminating the subjectivity of manually set parameters and significantly enhancing the intelligence of detection processes. Conventional machine learning techniques, encompassing decision trees, random forests, and artificial neural networks, have been extensively employed in cloud classification applications [19; 20; 21]. However, traditional machine learning methods still primarily rely on spectral features and require manually designed features. The swift evolution of deep learning has catalyzed the pervasive implementation of artificial neural networks in cloud detection, attributable to their remarkable generalization prowess across heterogeneous scenarios. In contrast to conventional threshold-based approaches, deep learning methods effectively transcend spatiotemporal constraints through data-driven paradigms. The paradigm's principal strength resides in its capacity for automated hierarchical feature learning directly from input images, facilitating fully integrated end-to-end model optimization [22; 23; 24]. Notably, the feature extraction mechanism employing Convolutional Neural Networks (CNNs) has demonstrably enhanced the detection accuracy for clouds. For instance, [PERSON] et al. [25] developed a temporal sequence-based methodology for cloud and shadow detection in multispectral imagery, innovatively integrating low-rank sparse decomposition with a Siamese network architecture. By constructing a composite reference image, this approach addresses the challenges posed by dynamic cloud distributions through the integration of DCM and MDFM, enabling robust differential feature extraction while mitigating semantic information attenuation during the decoding process. [PERSON] et al.