## 3 Experimental Results
### Comparative Experimental Results  
#### 3.3.1 Qualitative Evaluation  
As shown in Figures 6-8, three comparative experiments are illustrated. First, the cloud shadows are detected using the detection method. The results are shown in Figures 6b-8b. Then, other compensation methods such as linear correlation correction (LCC) [28], histogram matching (HM) [24], gamma transformation (GT) [29], corresponding shadow restoration (CSR) [44], and the Wallis and LCC combined method (WLC) [26] are selected as reference methods to compensate for the shadow area. The results are shown in Figures 6d-h-8d-h. They are compared with the those of the proposed method in this paper, as shown in Figures 6i-8i. In addition, Table 1 shows the quantitative compensation quality. forest area. Similarly, the results of the HM method show excessive contrast enhancement, resulting in contrast distortion. The CSR using non-shadow information from the global cloud shadow is not adaptive to different internal shadow regions. The compensation result shows a low contrast, so the detailed information in the shadow region is not recovered as it is in the non-shadow area. The WLC method combined the Wallis and LCC methods with internal window compensation. The contrast is improved too greatly to lose the real information. For example, the trees are over-recovered. However, the proposed method makes full use of different information levels for neutralization and compensates for such detailed information in a more balanced way. As shown in Figure 7, the cloud shadow covers most of the farmland area. Additionally, the farmland information is relatively simple. Thus, the internal shadow detection results have an inaccurate recognition problem in some shadow areas. Additionally, a non-shadow area between two adjacent cloud shadows is mistakenly identified as a shadow area. In this case, the non-shadow area is also directly increased in brightness through shadow compensation, leading to an obvious over-compensation problem. The LCC, GT, and CSR show a lower contrast in the compensation results. On the contrary, the WLC shows over-contrast compensation. The HM shows the difference between the dark and light area in the compensation result. As a result, if a non-shadow area is included in the shadow accidentally, it will affect the compensation results to make it more bright and to make the dark area darker. Nevertheless, the proposed method can enhance the brightness to make it more balanced. The over-compensation in the non-shadow area is effectively alleviated because the information of the super-pixel area around the pixel is fully utilized to offset the degree of brightness enhancement resulting from compensation. As shown in Figure 8, the building information in the shadow area is complex and changeable, and the details are more abundant. Moreover, there is an inconsistent shelter degree problem in the cloud shadow. The shelter degree of the edge is larger than that of the inner central region, leading to the problem that the inner region is relatively dark compared to other compensation results. The LCC and GT show similar compensation results. The contrast and brightness are improved but lower than the non-shadow object values. HM enhanced the contrast greatly so that the regions around the shadow edge are over-compensated while the central region is insufficiently compensated. CSR shows a lower contrast improvement, although it can enhance brightness. However, the detailed information can not be restored well. The shadow area consists of much detailed information. If the local information is not considered in the compensation model, the thickness of the cloud will affect the compensation results. WLC also used the window information unsuitable because the contrast is over-compensated. Therefore, if the local region is unsuitable, the compensation results will be abnormal. Nonetheless, the method proposed in this paper balances the problem of inconsistency in compensation. The overall compensation effect is very uniform.