## 4 Conclusions  
This paper proposes a novel method for performing the normalization of radiometric properties due to atmospheric conditions and phenological properties due to changes in vegetation for high-resolution \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **Method** & **Band 1** & **Band 2** & **Band 3** & **Band 4** \\ \hline \multirow{4}{*}{Site 2} & ExG & **0.1248** & 0.1747 & 0.2730 & **0.2771** \\  & ExGR & 0.1289 & 0.1733 & **0.2579** & 0.2842 \\  & VEG & 0.1294 & 0.1762 & 0.2702 & 0.2848 \\  & CIVE & 0.1285 & 0.1703 & 0.2686 & 0.2843 \\  & COM & 0.1262 & **0.1696** & 0.2635 & 0.2848 \\ \hline \multirow{4}{*}{Site 3} & ExG & **0.1469** & 0.2187 & 0.3600 & **0.1909** \\  & ExGR & 0.1493 & 0.2171 & **0.3315** & 0.1950 \\ \cline{1-1}  & VEG & 0.1521 & 0.2222 & 0.3728 & 0.1949 \\ \cline{1-1}  & CIVE & 0.1489 & 0.2169 & 0.3586 & 0.1950 \\ \cline{1-1}  & COM & 0.1494 & **0.2093** & 0.3621 & 0.1949 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 12: The experimental images for the additional dataset: (**a**) subject image acquired on 23 February 2017, (**b**) reference image acquired on 09 October 2015, and (**c**) normalization result of the proposed method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **Method** & **Band 1** & **Band 2** & **Band 3** & **Band 4** & **Average** \\ \hline \multirow{2}{*}{Seoul} & Proposed & \multirow{2}{*}{0.1305} & \multirow{2}{*}{0.1773} & \multirow{2}{*}{0.2639} & \multirow{2}{*}{0.1874} & \multirow{2}{*}{0.1898} \\  & method & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: NRMSE of the proposed method for the additional dataset. satellite images. MLP forms the basis of the proposed method, and a nonlinear relationship is assumed in order to normalize the aforementioned properties, in contrast to the approach that is used by conventional relative radiometric normalization algorithms. The proposed method performs radiometric resolution compression for subject and reference images, and 8-bit is selected when considering the complexity and time cost. Subsequently, RCSS is extracted based on the NC method, and a spectral index for each band is selected (ExG, COM, ExGR, and ExG). Phenological normalization is performed by establishing a nonlinear relationship through MLP. Regarding MLP, the number of hidden layers is fixed at one, when considering the algorithm generalization and the training time limits, while the number of the layer's neurons is determined through experiments. Generally, the higher the number of neurons for the hidden layer, the greater the network capacity required to train the model. However, in this study, it is confirmed that, as the number of neurons for hidden layer increased, the training time increased, while the accuracy decreased. In other words, networks with high complexity have poor generalizations and adverse effects as compared to small and simple networks. Thus, three neurons for the hidden layer are selected. Finally, the global radiometric properties are adjusted through postprocessing. The proposed method is then compared with conventional methods (MS, NC, histogram matching, and RF), which shows superior results via both visual inspection and quantitative evaluation. Visually, this finding suggests that the concepts that are mentioned above are suited to normalize radiometric and phenological properties. The methods that assume linear relationships cannot normalize the radiometric and phenological properties at all. When assuming a nonlinear relationship, if only statistical information is used, the radiometric properties are normalized, but phenological normalization cannot be performed. In the RF approach, radiometric and phenological normalizations are performed, but with a loss of information, such as spectral distortion and blurriness, which is due to neighbor information-based features. Furthermore, to analyze the radiometric and phenological properties in detail, some areas are enlarged.