# Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization
## 4 Experiment  
### Baseline Study for Spatial Information Mismatch  
To confirm the limitation of current methods under color space difference and pixel mismatch, we applied the method of [PERSON] et al. [28] to the cases where pixel mismatch exists, and color spectral functions are not identical for **H** and **I**. The method of [PERSON] et al. implicitly assumes that there is no pixel mismatch or color difference between high resolution RGB image and low resolution HSI, and the effects of this assumption are investigated using images with pixel mismatch and color difference. A public hyperspectral database, called the Harvard dataset [34], was used for the evaluation of the proposed algorithm. Because the purpose of the Harvard dataset [34] is to establish the basic statistical \begin{table}
\begin{tabular}{l} \hline \hline
**Requires:** \\
**H (low-resolution hyperspectral image)** \\
**I (high-resolution RGB image)** \\
**C (RGB camera sensitivity)** \\
**S (upsampling rate)** \\ \hline \hline \multicolumn{2}{l}{Reconstruct **L** by applying **C** to **H**} \\ Match histogram of **I** to that of **L** \\ Initialize **E\({}^{(0)}\)** with SISAL and **A\({}^{\prime(0)}\)** with SUnSAL from **H** \\ Initialize **A\({}^{(0)}\)** by upsampling **A\({}^{\prime(0)}\)** with **S** \\ \(k\gets 0\) \\
**while** not converged **do** \\ \(k\gets k+1\) \\
**A\({}^{(k)}\leftarrow\textbf{A}^{(k-1)}\textbf{S}\); Estimate **E\({}^{(k)}\)** with (8a) and (8b)** \\
**end while** \\
**return** \(\textbf{Z}=\textbf{E}^{(k)}\textbf{A}^{(k)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall algorithm for the proposed solution. structure of HSIs of real-world scenes, the dataset is in accord with the condition in which the proposed algorithm will be used. The Harvard dataset [34] has 50 indoor and outdoor images recorded under daylight illumination, and 27 images recorded under artificial or mixed illumination. The spatial resolution of the images is \(1392\times 1040\) pixels, with 31 spectral bands of width 10 nm, from 420 to 720 nm. The original HSIs are used as ground truth for the evaluation. First, the upper and left sides of the original HSIs (Figure 2a) were cut off by 50 pixels in both horizontal and vertical directions to form the pixel mismatch dataset (Figure 2b). Comparing Figure 2a,b, it can be observed that the location of the window on the left side is slightly translated, potentially due to the lower per-pixel performance of the super-resolution process. Second, RGB images were reconstructed as displayed in Figure 2c, using a color spectral function, Nokia N900, which was different from the one used in optimization. The camera sensitivities of the Nokia N900 and Nikon D700 are displayed in Figure 3. Figure 2c has relatively brighter color space compared to Figure 2a, which causes low spectral accuracy of a super-resolution result. The maximum RGB value of the test was normalized to 1, which is a common image processing technique for enhancing the visibility of a reconstructed image. The experiments were conducted with the same implementation details as in [28] for both datasets, but the operation was forced to run 1500 iteration for the 11 selected images. For a fair comparison between the proposed algorithm and other methods, the same error rate method was used as the primary metric.