## 1 Introduction
Then, cloud-based methods are mainly designed for middle-resolution images, and the object information is replaced by the information from other temporal images, other bands or adjacent regions [20; 21]. The information in itself would be abandoned. Shadow-based cloud removal methods are suitable for high-resolution remote sensing images. The object's information in the cloud shadow is more clear than that in lower-resolution images. Therefore, shadow compensation methods for ground objects can be applied for cloud shadow removal. The shadow-based method can be divided into the image enhancement method and the model method [22; 23]. These methods do not dependon other image information. Therefore, it is more suitable for the shadow information recovery of high-resolution images. Image enhancement methods mainly use linear stretching, histogram matching [24], logarithmic transformation [25], walls filtering [26], and other image enhancement principles to improve the brightness of a shadow area. The effect of this method is related to the ability of the image enhancement algorithm. Additionally, the parameters are often determined based on experience. Consequently, achieving adaptive processing according to the change in the shadow shelter degree is challenging. In addition, the model method mainly adopts linear correlation correction (LCC) [27; 28], gamma correction [29], the color constancy principle [30], the illumination compensation model [31], and other methods to establish the shadow illumination compensation model. It adds information on non-shadow areas into the compensation model to enhance the compensation of shadow areas. The linear correlation compensation principle [32] is similar to that of local statistical enhancement. They mainly utilize the statistical characteristic mean and variance of a non-shadow area as the target value to establish the compensation model. The principle of this method is simple and effective. On this basis, many methods exist to modify the models, such as by increasing the compensation intensity coefficient and automatically obtaining the compensation parameters [33]. [PERSON] et al. [34] adopt the SVM classification method for shadow detection on roads. Through region matching, it finds the local road unit in a shadow area and non-shadow area to select compensation parameters and complete adaptive compensation accurately. The deep learning method has recently been adopted for shadow removal in high-resolution images [35]. [PERSON] et al. [36] developed a recurrent shadow attention model (RSAM) to retrieve fine-scale land cover classes within cast shadows and self-shadows along the urban-rural gradient. However, the requirement of sample labels for the model's construction restricts its application. With the development of satellite imaging technology, the resolution of remote sensing images is improving gradually. As a result, the influence of a cloud and cloud shadow in high-resolution images has increased. Moreover, the problem of uneven shading in cloud shadows becomes greater as the resolution increases. Cloud-based and shadow-based methods are not able to solve the issue of uneven information loss in cloud shadows of high-resolution images yet. Since these problems are prone to occur in high-resolution images, shadow-based methods are more suitable than cloud-based methods are. However, the majority of automatic shadow compensation methods are based on global image analysis or shadow area unit analysis, which are often designed for shadows of a ground object. When they are directly applied in cloud shadow compensation, a large area of cloud shadow is usually compensated for as a whole. At this point, the compensation model in the same area unit is unchanged. Additionally, compensation will be improved to the same extent in the inconsistent shelter area. Thus, insufficient or excessive compensation problems easily result. Therefore, this compensation method cannot reflect the differences within a region or solve the problem of uneven shelter within a cloud shadow.