## 3 Experimental Results and Discussion
### Results and Analyses  
In this sub-section, we report the experimental results conducted on the four datasets. The experiments were conducted under different resolution ratios, i.e., the HS image was down-sampled by the facts of 2, 4, and 6 (i.e., \(s\) = 2, 4, and 6), respectively, as Section 3.1 mentioned. The numerical results are listed in Tables 1-3, respectively, where the best values are highlighted in bold. For CNN-based approaches, the training and testing times are listed separately in Tables 1-3. By observing the SAM, ERGAS, and UIQI values, we can see that among the conventional matrix factorization-based methods, JNMF and CNMF provide similar performance for these datasets on the whole, whereas DPLM has an impressive ability to alleviate the spectral distortion that might be caused by different imaging conditions, since it had outstanding performance on the San Francisco dataset. The CNN-based algorithms, namely the last three methods, obviously surpass the conventional methods in totally minimizing the fusion errors, as in most cases, they achieved far more desirable results. For example, the SAM and ERGAS values of San Francisco dataset under a resolution ratio of 2 are much lower than the former three methods, whereas the UIQI values are much higher. The presented CpCNN approach exhibits the best spectral fidelity performance, since in most cases, it achieves lower fusion errors in both angle (spectrum shape) and amplitude aspects and higher image quality. By comparing Tables 1-3, it can be clearly seen that with the decrement of HS image resolution (i.e., \(s\) varies from 2 to 6), the qualities of fused images undoubtedly degenerate. Nevertheless, the CNN-based approaches remain remarkable in performance, especially for the detail-injection methods. It should also be recognized that, in fact, the resolution decrement also results in a decrement of training samples, since the HS and MS images in the degraded scales contain substantially fewer pixels, which certainly affects the training of networks. In summary, these tables indicate that the proposed CpCNN is significantly preferable to the other approaches in spectral characteristic preservation aspect. Additionally, the running times in the tables suggest that JNMF seems to be the most time-saving method in our experiments. The CNN-based approaches are generally composed of training and testing stages. We can see that the training stage of the network always takes a considerably long time, which highly depends on the architecture of the network and the number of samples. Once the network is trained, we will not spend much time inferring the high-resolution maps. In addition, thanks to the small patch and filter sizes, the training time of our network is far less than TCNNF and DiCNN, although it is still time-consuming compared with the conventional methods. Figures 2-5 show the false-color images of fusion results under a resolution ratio of 4 (i.e., s = 4). For the first dataset, the numerical evaluation suggests that all of the fused images have high similarity with the reference, e.g., the largest SAM and ERGAS among different approaches are 4.73 and 3.55, respectively, whereas the worst UIQI is 0.9709; thus, we can see that the colors of these images in Figure 2 are close to the reference image