# A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery
## 3 Results and Discussion  
### Comparison of Fusion Results  
To evaluate the effectiveness of the proposed fusion approach, its results were compared with those of the conventional image-fusion algorithm. To ensure a fair comparison, fusion algorithms using a single SAR and panchromatic image were considered, where the a-trous wavelet decomposition (ATWD) [20], DWT [23], NSCT [19], and NSCT-pulse couple neural network (NSCT-PCNN) [5] methods were selected. The ATWD method is based on the importance of the wavelet coefficient, which is incorporated into the SAR image at a certain high frequency. For the DWT method, the maximum values of the coefficients at low frequencies and high frequencies are selected as the low and high frequencies, respectively. The NSCT method is based on the contourlet transform without downsamplers and upsamplers, and it also selects the averaging scheme at a low frequency and the maximum scheme at high frequency. The NSCT-PCNN method performs fusion based on PCNN for coefficients at low frequencies, and the coefficients at high frequencies are obtained through NSCT. In accordance with the aforementioned details, the decomposition level of NSCT and NSCT-PCNN was selected as the three showing the best image-fusion results [19]. Furthermore, the experiments were carried out on a desktop PC with an Intel(R) Core (TM) i7-8700 @ 3.20 GHz processor, 24.00 GB of RAM, and a 64-bit Windows 10 operating system. Particularly, all experiments involving the proposed model were programmed in Python 3.7, and the conventional methods were programmed in MATLAB 2019a. The image-fusion results are shown in Figures 6-8. Figure 6: Comparison with the results of image fusion for Site 1: (**a**) proposed method; (**b**) a-trous wavelet decomposition; (**c**) discrete wavelet transform; (**d**) non-subsampled contourlet transform; and (**e**) non-subsampled contourlet transform-pulse couple neural network. Figure 7: Comparison with the results of image fusion for Site 2: (**a**) proposed method; (**b**) a-trous wavelet decomposition; (**c**) discrete wavelet transform; (**d**) non-subsampled contourlet transform-pulse couple neural network. From an overall visual inspection, the results of the proposed method and those of the conventional fusion methods provided more information than the original single image. They contained spatial information of the panchromatic image, such as the line information and edge information of buildings, as well as the object information of the SAR image. However, in the results of ATWD and DWT, spatial information was insufficient compared with those of other methods. For Site 1, which primarily consisted of vegetation and included developed structures, the surface roughness of the SAR image in both these areas was emphasized, resulting in less spatial information. Sites 2 and 3 mainly consisted of developed structures, and the surface roughness of the SAR image was also emphasized more than the line and edge information of buildings, like the results for Site 1. Furthermore, more spatial information was present in the result of the NSCT than in the result of ATWD or DWT; however, it was also confirmed that the object information of the SAR image was lost compared to the original SAR image. The result of the NSCT-PCNN included more spatial and object information compared to those of the conventional image-fusion methods; however, the spatial information of the vegetation in Site 1 was somewhat insufficient. In contrast, the proposed method included sufficient spatial and object information regardless of vegetation or developed areas. The specific details are indicated on the red rectangle in Figures 6-8, and the enlarged areas are shown in Figure 9.  
Figure 8: Comparison with the results of image fusion for Site 3: (**a**) proposed method; (**b**) a-torus wavelet decomposition; (**c**) discrete wavelet transform; (**d**) non-subsampled contourlet transform; and (**e**) non-subsampled contourlet transform-pulse couple neural network.