## 4 Conclusions  
Cloud shadows are very common phenomea in high-resolution remote sensing images. The shaded information loss of ground objects varies with the thickness of the cloud. Performing cloud shadow compensation to recover the shaded information is very useful for further image utilization and analysis. However, the traditional shadow compensation method does not take the following problem into consideration: that there are over-compensation and insufficient compensation results obtained using these methods. Therefore, this paper designs a balanced compensation method integrated with multi-level information to solve the problem of unbalanced information loss in cloud shadow regions. The compensated image can be further used for other applications such as image interpretation, object recognition, and so on. By combining the information of non-shadow and shadow areas, super-pixel, and pixel, the traditional compensation model of linear correlation correction is improved. Thus, the compensation results can balance the unbalanced compensation of the shadow area more effectively. The reasonably homogeneous pixel regions can be obtained via super-pixel segmentation. Additionally, they can be incorporated into the compensation model to reflect the local information of shadow pixels accurately. Therefore, it is an effective way to balance and mitigate the problem of imbalanced compensation. The experimental results show that the proposed method in this paper can effectively and reasonably balance the problems of unbalanced compensation, such as insufficient compensation and excessive compensation a the shadow area. It makes the detailed presentation ability more balanced and closer to the effect of that of the non-shadow area from global and local feedback, which leads to the realization of balanced cloud shadow compensation. This algorithm can be used as an image preprocessing step to improve the image and its related production quality.  
Our main effort is to solve the problem of information loss due to uneven shading caused by the cloud shadow. This method allows the effective use of the super-pixel  
Figure 12: Comparison of different shadow compensation results using based on different cloud shadow masks. (**a1**) orginal shadow mask 1. (**a2**) mask 2 obtained by dilating 5 pixels. (**a3**) mask 3 obtained by dilating 10 pixels. (**b1**) LCC result of mask 1. (**b2**) LCC result of shadow mask 2. (**b3**) LCC result of mask 3. (**c1**) HM result of mask 1. (**c2**) HM result of shadow mask 2. (**c3**) HM result of mask 3. (**d1**) GT result of mask 1. (**d2**) GT result of shadow mask 2. (**d3**) GT result of mask 3. (**e1**) CSR result of mask 1. (**e2**) CSR result of shadow mask 2. (**e3**) CSR result of mask 3. (**f1**) WLC result of mask 1. (**f2**) WLC result of shadow mask 2. (**f3**) WLC result of mask 3. (**g1**) our result of mask 1. (**g2**) our result of mask 2. (**g3**) our result of shadow mask 3.  
local and regional global information to solve the problem of unbalanced compensation. However, we only discussed the cloud shadow obscurely in this paper. Since cloud shadows always existswith the clouds, differently from cloud shadows, thick clouds obscure ground information greatly. The traditional replacement strategy may change the real information. In order to ensure precision, the aim of future work should be to detect and compensate for clouds and cloud shadows together as accurately as possible.  
Conceptualization, Y.L. and X.G.; methodology, Y.K., X.G. and Y.Z.; writing--original draft preparation, Y.L. and X.G.; writing--review and editing, Y.L. and X.G.; supervision, Y.L., B.L. and Y.K.; funding acquisition, Y.L., X.G., B.W. and Y.K. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest.