## 5 Conclusions  
In this work, we propose an adaptive shadow compensation method for hyperspectral images based on multi-exposure fusion and edge fusion. The method converts images to HSI color space to map shadows based on their unique high hue and low intensity, then uses an adaptive threshold to identify shadow regions. In the compensation phase, we use multi-exposure fusion for overall improvement and merge compensated shadows into the original image through edge fusion, which is guided by shadow detection results. Exposure coefficients for multi-exposure fusion are adaptively derived from the reflectance differences between shadowed and non-shadowed areas. Fusion weights consider the correlation between bands, utilizing spectral variance as the weight to reflect intensity variations within the spectral neighborhood, which to some extent addresses the issue of nonlinear attenuation caused by shadows across different bands. Edge fusion uses a  
Figure 11: The impact of fine-tuning parameters on shadow detection. Based on the Airport dataset, the change in the shadow detection result (**a**) of the original; (**b**) with the minority of the background removed; (**c**) with the majority of the background removed. Based on the Houston dataset, the change in the shadow detection result (**d**) of the original; (**e**) with the minority of the background removed; (**f**) with the majority of the background removed. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **MSR** & **SC-Cyc.** & **MF** & **ISR** & **Ours**1  & **Ours**2  \\ \hline Airport & 68.21 & 2375.14 & 13.22 & 921.35 & 19.25 & **8.17** \\ Houston & 237.31 & 101,723.52 & 172.48 & 6632.19 & 234.23 & **101.18** \\ \hline \hline \end{tabular}  
Lower running time, with optimal values in bold. 1 Before improvement. 2 After improvement.  
\end{table} 
Table 3: The runtime (s) of different methods based on the Airport and Houston datasets. p-Laplacian operator, based on image gradients, to identify pixel transitions near shadow edges. It selects optimal pixels for replacement, guided by spectral similarity and spatial distance, seamlessly merging shadowed and non-shadowed regions. To validate the effectiveness of our method and the spectral fidelity of various approaches, a new hyperspectral image dataset is proposed. The experimental results indicate that our method has higher spectral fidelity. Our method demonstrates competitive results in the improved downstream classification task. Adaptability and computational efficiency are the focus of our future work. [PERSON] and [PERSON] proposed the method and performed the experiments. [PERSON] guided the research and processed the dataset. All authors wrote the article. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No. 62372284). Not applicable. The Houston dataset for hyperspectral image shadow compensation is available in: [[https://hyperspectral.ee.uh.edu/?page_id=459](https://hyperspectral.ee.uh.edu/?page_id=459)]([https://hyperspectral.ee.uh.edu/?page_id=459](https://hyperspectral.ee.uh.edu/?page_id=459)) (accessed on 22 September 2022). The Airport dataset is available in: [[https://github.com/yan-m1014/Airport-Dataset](https://github.com/yan-m1014/Airport-Dataset)]([https://github.com/yan-m1014/Airport-Dataset](https://github.com/yan-m1014/Airport-Dataset)) (accessed on 20 April 2024). Our appreciation goes to [PERSON] from the Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, for providing the Airport dataset utilized in this study. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: