## 1 Introduction  
With the successive launches of high-resolution remote sensing satellites such as Jilin-1, WorldView-2, GeoEye-1, WorldView-3, GF-6, GF-7, etc., the spatial resolution of satellite remote sensing images has been continuously upgraded, which presents richer information and clearer details of land features. Shadow is a common phenomenon in remote sensing images, which is caused by direct sunlight being obscured by objects such as clouds and ground bumps. Shadows obscure critical land features, reducing image quality and readability [1]. In order to fully utilize the information provided by shadows [2; 3; 4; 5] and eliminate the influence of shadows on the subsequent processing and application of remote sensing images, it is necessary to perform shadow detection. Current shadow detection methods can be broadly categorized into three types: model-based methods, attribute-based methods, and deep learning-based methods. Model-based shadow detection methods can be divided into two categories based on the data used: image model-based and elevation data-based. Image model-based methods extract information from the image itself to compute the parameters of the model [6; 7; 8], and then a multi-step shadow detection procedure based on the proposed model can be designed. However, when the image conditions do not satisfy the hypothesized situation, the established model may be invalid. Elevation data-based methods construct a three-dimensional light source irradiation model on the basis of elevation data to predict possible shadow areas in the image [9; 10]. This approach typically requires a priori scene information such as sensor positions, camera imaging parameters, light source direction, and object geometry, which is represented by the Digital Elevation Model (DEM). [PERSON] et al. [11] combined DEM and Sun position to detect shadows in Very High Resolution (VHR) remote sensing orthophotos and refined the coarse shadow mask generated by the high-resolution DEM with morphological operations to obtain a fine shadow mask, which is suitable for shadow detection in complex urban environments. However, the results are dependent on the accuracy of DEM and the selected parameters. While model-based shadow detection methods effectively distinguish shadows from water bodies, their performance depends heavily on the accuracy of input parameters and elevation data utilization. Attribute-based shadow detection methods extract shadows by analyzing the differences in the spectral, texture, contextual information, geometry, and other attributes between the shadowed regions and non-shadowed regions in images [12; 13; 14; 15; 16; 17; 18; 19]. Compared to model-based methods, these methods usually do not require the inclusion of a priori information and only perform shadow detection based on existing image features. Because the image features are relatively easy to obtain, attribute-based methods have a wide range of applications. [PERSON] et al. [20] proposed a shadow detection method based on the Logarithmic Spectral Ratio Index (LSRI) by considering the data compression characteristics of logarithmic operations. [PERSON] et al. [21] proposed the Normalized Color Difference Index (NCDI) and Color Purity Index (CPI) by analyzing the different reflectance characteristics of shadows, vegetation, and water in visible and near-infrared wavelengths, which can effectively enhance shadows by suppressing the characteristics of dark features. [PERSON] et al. [22] combined the Simple Linear Iterative Clustering (SLIC) method and LSRI to detect three types of shadows in mountainous areas caused by terrain, clouds, and buildings, which achieves good detection results. However, if there are dark water bodies and vegetation in the image, the method still cannot avoid the problem of shadow misdetection. Overall, attribute-based shadow detection methods can accurately locate the boundary and outline of shadow regions by analyzing the detailed attributes of the image using color, texture, and gradient information.