# A Spectral-Aware Convolutional Neural Network for Pansharpening
## 4 Experiment Results
### Experimental 4--Pavia University Dataset  
This dataset covers a urban area in Pavia, northern Italy. Limited to the scant spatial size of the dataset, we choose a sub-scene with \(150\times 75\) pixels for testing. Considering the large-volume data and the calculation efficiency, a dimensionality reduction operation is applied to this dataset when it is used in the CNN-based methods. Figure 4: Spectral difference curves between the ground truth and the pansharpening results for the WorldView-2 dataset at randomly selected spatial coordinates: (**a**) (64, 32); (**b**) (53, 169); (**c**) (100, 55); and (**d**) (200, 202), which are marked in Figure 3a with red crosses. Figure 5: Pansharpening results for the IKONOS dataset: (**a**) ground truth; (**b**) EXP; (**c**) PCA; (**d**) CSA; (**e**) PRACS; (**f**) ATWT; (**g**) MTF-GLP-HPM; (**h**) CNMF; (**i**) PNN; (**j**) DRPNN; (**k**) DiCNN1; and (**l**) SA-PNN. Figure 6: Pansharpening results for the Quickbird dataset: (**a**) ground truth; (**b**) EXP; (**c**) PCA; (**d**) CSA; (**e**) PRACS; (**f**) ATWT; (**g**) MTF-GLP-HPM; (**h**) CNMF; (**i**) PNN; (**j**) DRPNN; (**k**) DiCNN1; and (**l**) SA-PNN. Table 5 presents the results of the reduced resolution quality assessment on the Pavia dataset. As we can see, although many traditional methods like GSA and Bayesian Sparse have better SCC than EXP, they are poor in guaranteeing the spectral fidelity due to the greatly increased number of bands. SFIM and MTF-GLP-HPM achieve relatively good performance. In the CNN-based methods, PNN gets high SCC while the other three metrics are very bad, which implies that PNN is not effective in improving spectral fidelity when used on data with a large number of bands. The results of DRPNN and DiCNN1 are competitive, with significant improvement compared to PNN. Our SA-PNN still achieved the best pansharpening quality. Visual inspection is also given in Figure 7. Obviously, the result of GFPCA is severely blurred while Bayesian Sparse shows spectral distortion such as the area in the upper left of the images in Figure 7. Other traditional methods like SFIM and CNMF achieve a trade-off between spatial details and spectral fidelity. Among all methods, our SA-PNN gets the best visual result. Considering that the simulated pseudo-color visual results shown in Figure 7 may not fully exhibit the spectral characteristics of the data with a large number of bands, for the CNN-based methods, we also show the spectral difference curves at randomly selected spatial coordinates: (8, 27), (8, 125), (64, 11), and (68, 131) in Figure 8. It is obvious that the spectral difference curves of PNN deviate heavily from the reference line, which corresponds to the spectral distortion presented in the pansharpening result of PNN. The overall spectral difference curves of our SA-PNN are the most \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Q4** & **Q** & **SAM** & **ERGAS** & **SCC** \\ \hline Reference & 1 & 1 & 0 & 0 & 1 \\ \hline EXP & 0.5914 & 0.5731 & 3.0248 & 2.8352 & 0.6693 \\ PCA & 0.7669 & 0.7055 & 3.0902 & 2.4352 & 0.8616 \\ GSA & 0.8747 & 0.8545 & 2.3774 & 1.7725 & 0.8954 \\ PRACS & 0.8370 & 0.8073 & 2.1989 & 1.7344 & 0.9023 \\ ATWT & 0.8685 & 0.8550 & 2.1692 & 1.6633 & 0.9064 \\ MTF-GLP-HPM & 0.8790 & 0.8674 & 2.0038 & 1.5398 & 0.9197 \\ CNMF & 0.6837 & 0.7198 & 2.7102 & 2.2655 & 0.8758 \\ PNN & 0.9414 & 0.9375 & 1.4584 & 1.0401 & 0.9669 \\ DRPNN & 0.9391 & 0.9360 & 1.4619 & 1.0505 & 0.9669 \\ DiCNN1 & 0.9414 & 0.9385 & 1.4260 & 1.0340 & 0.9681 \\ \hline SA-PNN & **0.9490** & **0.9465** & **1.3302** & **0.9715** & **0.9727** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quality indexes at reduced resolution on a \(256\times 256\) sub-scene of the Quickbird dataset (best result is in bold).