# Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization
## 5 Conclusions  
A new approach for hyperspectral super-resolution for an HSI and an RGB image taken with different camera sensitivities and fields of view is proposed. Matching different camera sensitivities and fields of view is a challenging task, that must be overcome to develop an HSI-RGB image integrated system for environments where this issue is inevitable. The proposed method employs a modification of the work of [28] to solve the super-resolution problem in an environment where an HSI and a target RGB image have slightly different fields of view. The basic principle of the proposed method is to obtain endmembers from the given his, and to optimize the per-pixel abundance with RGB images, for which the histogram is matched to that of the RGB image reconstructed from the HSI. The endmember optimization term between the low-resolution HSI and the high-resolution HSI is removed, to prevent divergence caused by different per-pixel abundances of the low-resolution HSI and the high-resolution RGB. A histogram-matching step between the given RGB image and the RGB image reconstructed from the HSI is added, to reduce the color difference caused by different camera sensitivities and the potential accuracy decrease caused by removal of the endmember optimization term.  
We first conducted two experiments to confirm the effect of different camera sensitivities and fields of view. The datasets for the experiments were constructed by editing 11 selected images from the Harvard dataset, which is comprised of basic statistical structures of HSIs of real-world scenes. To confirm the effect of the color difference, the input high-resolution RGB image is reconstructed using camera spectral functions different from the one used for the optimization process. To confirm the effect of the different fields of view, the upper and left sides of the input high-resolution RGB image are cut off by 50 pixels. The experimental results demonstrate that these differences may lead  
Figure 6: Example of per-pixel RMSE images and reconstructed RGB images of high-resolution HSIs: (**a**) Per-pixel RMSE image where a shear transformation with \(C_{x}=0.3\) is applied, (**b**) a per-pixel RMSE image where translation with \(P_{n}=80\) is applied, (**c**) RGB image an where shear transformation with \(C_{x}=0.3\) is applied, and (**d**) RGB image where translation with \(P_{n}=80\) is applied.  
to a high error rate in the super-resolution problem. The performance of the proposed method was also tested on the Harvard dataset. The experimental dataset was constructed by adding two types of geometric transformation, warping and transition, to the reconstructed RGB image, using camera spectral functions different from the one used for the optimization process. The low error rate of the experimental results in RMSE and SAM demonstrates that the proposed algorithm is applicable in environments with pixel mismatching and color differences.  
B.K. and S.C. conceived and designed the methods; B.K. developed the software; B.K. performed the validation and visualization of results; B.K. and S.C. wrote the paper; S.C. supervised the research. S.C. acquired the research funding.  
This research was supported by a grant (19 SCIP-C116873-04) from the Construction Technology Research Program funded by the Ministry of Land, Infrastructure, and Transport of the Korean government.  
The authors declare no conflict of interest.