## 4 Experimental Results
### Qualitative Comparison
GMC also improved the brightness and contrast efficiently; and while its contrast enhancement was better than OWC, it was not as good as the other methods, as shown in boxes B\({}_{2}\), E\({}_{2}\), and F\({}_{2}\). Because parameter \(\gamma\) could not be adaptive to every image and shadow area, it was hard to decide which value was best for them. Therefore, GMC's compensation capability was not stable and self-adaptive. The effect of GMC was different among the images; and therefore, sometimes it was good, as seen in Images 2 and 5, but mostly it was not good enough. HMT produced stable compensation results and was especially good at contrast enhancement; however, sometimes it was over-enhanced and lost the original information. As can be seen in box A\({}_{3}\), the shadow area was over-compensated in contrast. Comparatively, LCC, as one of the most typical compensation methods, undeniably recovered the primary information in the shadow area; and while it enhanced brightness and contrast efficiently, it was unable to achieve the same levels as in the non-shadow areas. LCC also produced color deviation and uneven improvements in different shadow areas; and its results show a blue color instead of the original color. This color difference was mainly caused by its over-enhanced illumination, resulting in a higher blue component than the non-shadow area. However, the color bios in cloud shadow were less serious than the ground object shadows since the difference between the compensated shadow area and the non-shadow area was low. In comparison, the results for our method represented the original roof color better due to our color correction strategy. Additionally, the problem of uneven compensation was present in most of the compared methods (e.g., the boxes in Images C and D). Box C\({}_{4}\) shows that LCC experienced inefficient and uneven compensation. Figure 9: Shadow compensation results in aerial Image 6. (**a**) Original image; (**b**) Shadow detection result (red); (**c**) The OWC result; (**d**) The GMC result; (**e**) The HMT result;(**f**) The LCC result; (**g**) The CRM result; (**h**) The OOPR result; (**i**) Our method’s result. Recent methods, such as OOPR and CRM, were designed to obtain self-adaptive parameters adjusted according to each shadow as well. Although they adopted their respective adaptive parameter strategies, they did not achieve stable results in the various images. OOPR utilizes a polynomial fitting compensation principle, which cannot solve the best function for each shadow region from the IPOOL strategy. As a result, OOPR improved the brightness similarity to the non-shadow area, but its contrast enhancement was inadequate due to its limited polynomial function. As shown in Image 3, the brightness was satisfied while the contrast did not meet the non-shadow level. In addition, although CRM also utilizes LCC as the compensation model, it gained the non-shadow information from the adjacent non-shadow segment areas of each shadow area. CRM produced overcompensation as shown in Image 4, Image C, and Image F. For example, the shadow in box F\({}_{5}\) is obviously over-compensated; and the contrast enhancement in Image F is worse than in Images C and D. When the segmentation areas were not suitable, the shadow area was over-compensated or insufficiently compensated, as shown in Image F. It is difficult to both improve the brightness and contrast at the same time in a way that is similar to non-shadow areas, which was a common problem for CRM and OOPR. In contrast, these problems were solved by our method, since it was able to adaptively recover the shaded information as clearly and initially as it would appear in sunlight. Since our approach uses different parameters for different shadow areas, the shadow information is strengthened more reasonably according to every shadow situation. The most significant benefit of this strategy is that it prohibits over-compensation or inefficient compensation.