# LSTNet: A Reference-Based Learning Spectral Transformer Network for Spectral Super-Resolution
## 4 Experiments and Results
### Comparison Experiment on Real Data  
We also evaluated the performance of the different methods on real data. The Arad method achieved suitable performance on the simulated data, but the target image could not be reconstructed when considering real data. This was mainly because the learning method of its dictionary was based entirely on HSI images, and it was incapable of reconstructing  
Figure 8: Visual comparison of the results for different methods on the University of Houston data set.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Arad** & **MSSR** & **FMNSSR** & **StiNet** & **HRNet** & **AWAN** & **Proposed** \\ \hline mPSNR & 51.2520 & 41.5942 & 49.4653 & 50.0773 & 44.5608 & 55.4147 & **55.4916**1  \\ SAM & 1.9206 & 3.0962 & 1.4491 & 1.1656 & 1.91728 & 0.9282 & **0.9107**1  \\ ERGAS & 5.0095 & 7.9960 & 3.8359 & 3.244 & 5.4504 & 2.4631 & **2.3767**1  \\ RMSE & 0.0046 & 0.0087 & 0.0036 & 0.003 & 0.0062 & **0.0024**1  & **0.0024**1  \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quality assessment comparison on the University of Houston data set.  
an HSI from an MSI if there was a radiometric difference between them. Therefore, the comparison experiments only focused on the deep learning SSR methods. As shown in Figure 9, the reconstruction results from the different methods had large variability. The MSSR had poor reconstruction performance for both spatial and spectral information. In particular, there was a large distortion between the reconstructed spectral information and the real spectrum. The reconstruction results of FMNSSR, StiNet, HRNet, and AWAN were similar: they all exhibited material-dependent and significant banding errors, which were especially evident in the MRAE and SAM error images. In contrast, there were no obvious banding errors in single-band reconstruction at 467, 522, and 651 nm. With the spectral transformer as a guide, the performance of the proposed method in spectral reconstruction was promising.  
Table 4 shows the reconstruction metrics for different methods. It is worth noting that the mPSNR of these data was lower than that for the simulated imagery. The main reasons were the relatively low spatial resolution of the GF5 image and the presence of a certain degree of noise in the first and last bands of the true value image. The low resolution of the image made the edges of the feature unclear, and the noise in the original image of GF5 made it so that the accuracy calculation did not reflect the real reconstruction effect. Nevertheless, the method proposed in this paper achieved optimal results in the reconstruction index.  
Figure 9: Visual comparison of the results for different methods on the GF5 data.  
In addition, we plotted the spectral curves to compare the spectral reconstruction effect of different SSR methods, as shown in Figure 10. Point 1 represents the comparison of spectral curves in bare soil areas, while points 2 and 3 indicate different types of vegetation. Compared with the other methods, the spectral curve of the proposed method was closer to the actual spectral curve. The main reason for this was the use of transfer learning in the proposed method, which migrated the spectral information from the reference image to obtain a more accurate reconstruction result.