# A Spectral-Aware Convolutional Neural Network for Pansharpening
## 4 Experiment Results
### Experimental 1--WorldView-2 Washington Dataset  
This dataset covers an urban area in Washington D.C., and we chose a sub-scene with \(256\times 256\) pixels for testing.  
Table 2 shows the results of the reduced resolution quality assessment on the WorldView-2 dataset. The best results are shown in bold. As we can see, PRACS and CNMF have worse ERGAS than other methods, which indicates the poor overall pansharpening quality. In contrast, ATWT and MTF-GLP-HPM achieve relatively high scores on all metrics. Other methods like PCA and GSA restore the spatial details to some degree, as reflected in the better SCC than EXP. However, they yield worse SAM, which is useful to quantify the spectral distortion. The CNN-based methods achieve better performance compared to the traditional methods. Among them, our proposed SA-PNN yields the best numeric assessment results, especially in the impressive performance gain of SAM.  
Although the numerical indicators show the performance of our proposed method clearly, we also focus on visual inspection to find noticeable distortions that elude quantitative analyses. As shown in Figure 3, the results of the traditional methods suffer from some spatial blur, such as the area in the upper middle of the images in Figure 3. Among them, CNMF is characterized by severe spectral distortions, such as the lake region in the bottom left of the images in Figure 3. The results of the CNN-based methods are more similar to the ground truth and our SA-PNN exhibits an excellent quality both in spatial detail and spectral fidelity. What is noteworthy is that the result of PNN shows spectral distortion at the edge, which is caused by convolution without padding during the training phase. This is unnoticeable in the numerical indicators.  
To further compare performance, the spectral difference curves between the ground truth and the pansharpening results, which are generated by subtracting the ground truth from the pansharpening  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **Training Time** \\ \hline WorldView-2 & 84 h 38 min \\ IKNOS & 52 h 49 min \\ Quickbird & 52 h 25 min \\ Pavia & 9 h 18 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average training time on four datasets (\(5\times 10^{5}\) iterations).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **QS** & **Q** & **SAM** & **ERGAS** & **SCC** \\ \hline Reference & 1 & 1 & 0 & 0 & 1 \\ \hline EXP & 0.6612 & 0.6658 & 7.3985 & 7.1582 & 0.5202 \\ PCA & 0.8910 & 0.8353 & 7.4365 & 5.1001 & 0.8532 \\ CSA & 0.8870 & 0.8774 & 7.6723 & 4.3403 & 0.8470 \\ PRACS & 0.8255 & 0.8211 & 7.4250 & 5.2171 & 0.7946 \\ ATWT & 0.8865 & 0.8884 & 6.9038 & 4.2476 & 0.8768 \\ MTF-GLP-HPM & 0.8914 & 0.8933 & 6.9649 & 4.0964 & 0.8863 \\ CNMF & 0.8372 & 0.8381 & 7.8598 & 5.1929 & 0.8753 \\ PNN & 0.9441 & 0.9450 & 5.3307 & 2.9946 & 0.9369 \\ DRPRN & 0.9460 & 0.9469 & 5.2403 & 2.9657 & 0.9400 \\ DiCNN1 & 0.9463 & 0.9472 & 5.1949 & 2.9575 & 0.9405 \\ \hline SA-PNN & **0.9527** & **0.9533** & **4.8667** & **2.7689** & **0.9500** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quality indexes at reduced resolution on a \(256\times 256\) sub-scene of the WorldView-2 dataset (best result is in bold).  
results, are plotted in Figure 4. Specifically, the values along the difference axes in Figure 4 indicate the difference in gray values between the ground truth and the pansharpening results. As we can see, while performance for individual spatial coordinates varies, on average, when examining multiple coordinates and bands, the spectral difference curves of SA-PNN closely approximate the reference, demonstrating that SA-PNN offers superior spectral fidelity.