## 5 Conclusions  
In this work, a detection method based on Cayley-Klein measure and a prior shape information is proposed for better reorganization of underwater targets, which leads to satisfactory outcomes in processing the hyperspectral images captured by UAV. Then, both experiments and comparative analysis are conducted to draw the following conclusions.  
Firstly, the Mahalanobis distance can mitigate the impact of background on target identification. Meanwhile, the hyperspectral images are mapped from the high-dimensional space to the low-dimensional space, thus improving the efficiency of data processing. However, this distance is effectively a linear mapping, which means it can fail in high-dimensional space. To address the constraint on processing high-dimensional data with the Mahalanobis distance, we propose to use the Cayley-Klein measure to improve efficiency.  
Secondly, the Cayley-Klein measure proposed in this study takes full advantage of global and local information to suppress the background, which enhances the discrimination between the target and the background while improving the heterogeneity of the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Experimental Area**} & \multirow{2}{*}{**Repeated Time**} & \multicolumn{6}{c}{**Computational Cost (s)**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**Proposed Algorithm**} & \multirow{2}{*}{**Kernel IF**} & \multirow{2}{*}{**VABS**} & \multirow{2}{*}{**SFBA**} & \multirow{2}{*}{**SSFAD**}  
target area. This is conducive to the identification of targets. Compared with traditional algorithms, the algorithm proposed in this work is applicable to detect the targets at a water depth of up to 5 m.  
Thirdly, based on the prior shape information of the underwater target, a false target detection algorithm is developed to improve the accuracy of target identification, which reduces the impact of the false positive rate.  
Lastly, the proposed algorithm achieves a high computational efficiency that is comparable with other mainstream target detection algorithms, and a good balance is reached between efficiency and accuracy.  
There are limitations of the algorithms proposed in this work because only spectral information is used to conduct underwater target detection and given the account of properties of electromagnetic wave transmission in water. Therefore, it is worthwhile to further explore how to make use of the combined information about spectrum, the intensity of light, and polarization. Another research direction would be to research the relationship between the detected water depth of the underwater target and the water quality.  
Conceptualization, B.Z. and Z.M.; methodology, B.Z., F.Z. and X.L.; investigation, Y.S. and L.L.; writing--original draft preparation, P.L.; writing--review and editing, B.Z.; supervision, Z.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 62005318.  
Not applicable.  
The authors would like to thank the editors and anonymous reviewers for their valuable comments that can greatly improve our manuscript.  
The authors declare no conflict of interest.