# Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression
## 3 Results and Discussion  
### Implementation and Performance  
This section presents the colorization results of the proposed algorithm and compares these with the results of other state-of-the-art colorization algorithms. To ensure a fair comparison, colorization algorithms that use only a single-color image as the reference (exemplar-based method) and that are based on RF regression are compared, so that the methods of [PERSON] et al. [1], [PERSON] et al. [14], [PERSON] et al. [15], [PERSON] et al. [21], and [PERSON] et al. [22] are selected. When performing the colorization using these other methods, we used the same parameter settings suggested by their respective authors. In addition, two of the latest deep learning-based methods [18; 19] are included for visual comparison, using the codes provided by the authors. The results of the various algorithms are compared by visual inspection (see Figures 6-9) and quantitative evaluation with ground-truth images, which are the actual color images of the input grayscale images. Figure 6: Comparison with existing state-of-the-art colorization methods at site 1. From the overall visual inspection, the proposed algorithms appear to show better results than do the five existing methods for most cases. The [PERSON] et al. method [1] is based on transferring color from one initial color image considered as an example. Color prediction is performed by minimizing  
Figure 8: Comparison with existing state-of-the-art colorization methods at site 3. Figure 7: Comparison with existing state-of-the-art colorization methods at site 2. Figure 9: Comparison with existing state-of-the-art colorization methods at site 4.  
the distance in the simple statistics of a luminance image. However, as the results show, only a few colors are dealt with, and the results include many artifacts due to the lack of any spatial coherency criteria. In the case of the [PERSON] et al. method [14], image colorization is solved by computing the colors using different features and associated metrics. Then, the best colors are automatically selected via a variational framework. Overall, the results fail to select the best colors and have a desaturating effect, confirming the limitations of the variational framework. The [PERSON] et al. method [15] performs image colorization by automatically exploiting multiple image features. This method transfers color information by performing feature-matching between a reference color image and an input grayscale image--a process that is critical to the quality of the results. This achieves better colorization than the other exemplar-based methods but still only deals with a few colors, resulting in incorrect matches in challenging scenarios. The other [PERSON] et al. method [21] is a learning-based method in which learning is performed in superpixel units to enforce higher color consistency. Superpixels are extracted, features are computed for each superpixel, and learning is performed based on an RF regression. The results of this method contain more color information than do the results of the other exemplar-based methods, but the approach still does not retrieve certain colors such as blue or red (Figure 7), and it sometimes predicts completely different colors (as in Figures 8 and 9). The [PERSON] et al. method [22] is also based on RF regression, which is performed within the LEArning to seaRCH (LEARCH) framework. Furthermore, histogram correction is performed on the colorization image to improve the visual appeal of the results. In the state-of-the-art-methods used for comparison, this method predicts sufficient color information. However, halo effects exist in the object boundaries, especially in Figure 7. Moreover, as shown in Figure 9, the more complex the structure, the more halo effects are added, leading to many artifacts. In addition to exemplar-based and RF regression-based methods, deep learning-based methods [18; 19] are used for comparison.