# A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery
## 4 Results and Discussion
### Comparison with IOEM Method  
The proposed method was compared with the IOEM method [31]. The IOEM method uses the external and internal parameter error from the hyperspectral camera for on-orbit calibration. Moreover, the IOEM method uses the established internal orientation element model to complete the registration of all spectral bands. Figure 11 illustrates the registration results for the IOEM method and the proposed method for the dataset of OHS-Guangxi, China. The true color composite images produced by the IOEM method exhibited an obvious ghost-shadow phenomenon. Conversely, the true color composite imagery produced by the proposed method clearly showed ground objects with sharper edges and details, and thus featured a better registration accuracy. The homonymous points were extracted from the adjacent spectral bands after registration. The coordinate displacement between homonymous points was then calculated using Equation (16) to estimate the registration accuracy between adjacent spectral bands. Figure 12 shows the registration accuracy of adjacent bands when using the two methods. The maximum error produced by the IOEM method was 0.65 pixels and the minimum error was 0.30 pixels. However, the maximum error produced by the proposed method was 0.57 pixels and the minimum error was 0.23 pixels. Different accuracies were achieved for the three experimental images. Relatively high errors were revealed in the image covering Xinjiang, China. Since there are large deserts and fewer tex  
Figure 11: Comparison of registration results using IOEM and the proposed method. (**a**) IOEM method; (**b**) Proposed method; (**c**) IOEM method; (**d**) Proposed method. of Xinjiang, fewer feature points were detected than the other two images. A relatively small number of correctly matched keypoints may lead to a decrease in registration accuracy. In total, 20 uniformly distributed homonymous points were manually selected for registration accuracy assessment between non-adjacent bands in B03, B14, B08, B28, B10, and B30, respectively. The coordinate deviation between homonymous points was calculated using Equation (16). The registration accuracy between non-adjacent bands was greater than one pixel for the IOEM method. The registration accuracy between non-adjacent bands was located at the sub-pixel level in the proposed method, as shown in Table 3. Lower RMSE values of the proposed method indicate that it can obtain higher registration accuracies than IOEM method. In this study, the structural similarity measure and cosine similarity measure were used to evaluate the similarity between registered spectral bands. The similarity measures were calculated with a size of \(100\times 100\) pixels. As shown in Figure 13, the structural and cosine similarity measures between B03 and B14, B08 and B28, and B10 and B30 were all greater than 0.80 for the three experimental datasets. The distribution of structural similarity index values is more concentrated in the Guangxi image than the other two experimental sites, while the cosine similarity index values are more concentrated in the Xinjiang image (Figure 13). This might be because the two indices measure the similarity of images in different aspects. Therefore, similarity measures should be used conjunctively to gain a comprehensive understanding of the registration results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**RMSE (Pixel)**} & \multirow{2}{*}{**Mean RMSE**} \\ \cline{3-4} \cline{5-6}  & & **B03-B14** & & **B08-B28** & **B10-B30** & **(Pixel)** \\ \hline \multirow{2}{*}{OHS-Arizona-USA} & IOEM & 1.68 & 2.02 & 1.60 & 1.77 \\  & Our method & 0.68 & 0.39 & 0.42 & 0.50 \\ \hline \multirow{2}{*}{OHS-Guangxi-China} & IOEM & 2.28 & 3.34 & 3.76 & 3.13 \\  & Our method & 0.52 & 0.54 & 0.53 & 0.53 \\ \hline \multirow{2}{*}{OHS-Xinjiang-China} & IOEM & 1.00 & 1.03 & 1.24 & 1.09 \\  & Our method & 0.58 & 0.72 & 0.49 & 0.60 \\ \hline \hline \end{tabular}
\end{table}