# Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression
## 4 Conclusions  
This paper presents a colorization algorithm for aerial imagery. The proposed method uses a reference color image with similar seasonal features at the same location as an input grayscale image. The color space of the reference color image is converted to Lab, and unchanged regions are selected by applying change detection to the input grayscale image and the L component of the reference color image, which serves as meaningful training data. Moreover, color relationships are established in direct correspondence between the feature descriptors of the input grayscale image and the a and b  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**PSNR (dB)**} \\ \hline
**Method** & **Our Method** & [1] & [14] & [15] & [21] & [22] \\ \hline Site 1 & 35.0906 & 26.9364 & 29.1172 & 30.6962 & 32.628 & 31.5879 \\ Site 2 & 34.8936 & 27.1564 & 26.2594 & 29.9741 & 31.6331 & 31.8681 \\ Site 3 & 31.8709 & 26.1595 & 28.0316 & 29.4565 & 30.369 & 31.0295 \\ Site 4 & 29.6542 & 23.5535 & 26.8302 & 27.1006 & 28.0476 & 24.2211 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative evaluation of algorithm performance using standard peak signal-to-noise ratio (PSNR). dB: decibel.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**NCD**} \\ \hline
**Method** & **Our Method** & [1] & [14] & [15] & [21] & [22] \\ \hline Site 1 & 0.0707 & 0.1472 & 0.127 & 0.0963 & 0.0858 & 0.1042 \\ Site 2 & 0.0716 & 0.1408 & 0.1573 & 0.1334 & 0.0929 & 0.1069 \\ Site 3 & 0.1098 & 0.2094 & 0.1722 & 0.1472 & 0.1386 & 0.1373 \\ Site 4 & 0.1244 & 0.2304 & 0.1677 & 0.1679 & 0.1422 & 0.1801 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative evaluation of algorithm performance using normalized color difference (NCD).  
components of the reference color image based on the RF regression. Finally, histogram specification is applied to the colorization image to improve the results and is compared with state-of-the-art methods. Experimental results for multiple sites show that our method achieves visually appealing colorizations with significantly improved quantitative performance. In other words, the proposed algorithm performs well and outperforms existing colorization approaches for aerial images.  
Future work will include other complex descriptors or features in order to retrieve more color values for complex structures. In particular, we intend to find the combination of features that best describes the characteristics of the aerial images for colorization. We will also extend our application of the technique by applying satellite images obtained from various sensors other than aerial images. Furthermore, to overcome the limitations that may prevent colorization from being performed when reference color images are unavailable, a method of using reference color images that are obtained with different sensors or contain different seasons or resolutions will be sought out. Finally, we plan to colorize past grayscale aerial images using a time-series approach, possibly by incorporating monitoring frameworks.  
All authors contributed to the writing of the manuscript. [PERSON] carried out the experiments and analyzed the results; [PERSON] designed the experiments and presented the direction of this study; [PERSON] supervised this study; [PERSON] provided scientific counsel.  
This research was supported by the Basic Science Research Program through the NRF (National Research Foundation of Korea) funded by the Ministry of Education [No. 2016R1D1A1B03933562].  
The authors declare no conflict of interest.