# A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery
## 4 Conclusions  
This study proposes a method that fuses high-resolution SAR and panchromatic images. A learning-based approach is adopted, and RF regression, which considers the differences in imaging mechanisms, forms the basis of the proposed method. The proposed method first selects the pixels to be used for learning and then performs classification on stacked SAR and panchromatic images to establish independent relationships for each class, thereby reducing the algorithm complexity. In particular, the number of classes is selected as six considering the land cover distributions and training time. Furthermore, to consider as many features as possible, various features are extracted from the SAR image, among which the Gabor filter and the mean and standard deviation of multiple window sizes are selected. Finally, image fusion is performed based on RF regression; then, the results are compared with those of conventional image-fusion methods. The following conclusions are obtained based on the results. First, from the visual aspect, the proposed method includes more of the object information of the SAR image and spatial information of the panchromatic image than conventional image-fusion methods. It is confirmed that sufficient information is included, regardless of vegetation and built-up areas. Second, the quantitative performance of the proposed method shows significant improvements. The performance evaluation verifies that the proposed method exhibits improved preservation of the information of the SAR and panchromatic images and results in less distortion when compared with conventional image-fusion methods. Third, when validating the RF regression model employed in the proposed method, it is confirmed that the predictive model is properly constructed. In addition, in the case of the variables selected, they contribute appropriately to the RF regression model. Finally, the applicability of the proposed model is verified by applying the proposed method to an additional dataset.  
In future studies, the following aspects should be considered. First, by obtaining and applying the method to a sufficiently wide range of seasonal and temporal images, it should be further verified. Second, the method's usefulness should be further confirmed through application to SAR and panchromatic images obtained from other sensors. Third, the performance of the RF regression process should be improved by further extracting and combining various features. Finally, its applicability should be investigated by applying the fused images to various applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site** & **FQI** & **AG** & **SF** \\ \hline Site 1 & 0.7967 & 24.0456 & 14.8655 \\ Site 2 & 0.7873 & 28.3798 & 18.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluations of the additional dataset (FQI: fusion quality index, AG: average gradient, SF: spatial frequency).  
Figure 13: The experimental images in the additional dataset (Site 2): (**a**) TerraSAR-X image acquired on 8 August 2019, (**b**) GeoEye-1 image acquired on 19 August 2019, (**c**) fusion result of the proposed method.  
**Author Contributions:** Conceptualization, Y.D.E.; methodology, D.K.S.; software, D.K.S.; validation, D.K.S; formal analysis, D.K.S.; investigation, D.K.S.; resources, D.K.S.; data curation, Y.D.E.; writing--original draft preparation, Y.D.E.; writing--review and editing, Y.D.E.; visualization, D.K.S.; supervision, Y.D.E.; project administration, Y.D.E.; funding acquisition, Y.D.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C1085618).  
**Conflicts of Interest:** The authors declare no conflict of interest.