## 3 Results and Discussion  
### Comparisons of Normalization Results  
This section presents the normalization results of the proposed algorithm and then compares them with the results of a conventional algorithm. First, our proposed normalization process is performed step-by-step for the experimental images, in which the radiometric resolution is compressed to 8-bits, and the spectral indices of the blue, green, red, and NIR bands are selected as ExG, COM, ExGR, and ExG, respectively. Subsequently, the results of our proposed method are compared with the conventional methods (MS, NC, histogram matching, and random forest (RF)) to evaluate them visually and quantitatively, and Figures 6-8 show the results for each experimental image. From the overall visual inspection, the normalization of the proposed method is shown to be better than that of the conventional methods. MS and NC, which are regression-based methods, assume that the relationship between the subject and reference images is linear. However, as shown in the results, neither the phenological properties nor the radiometric properties are normalized. In other words, it can be confirmed that it is not appropriate to assume a linear relationship in the case of high-resolution images, in which vegetation changes exist. In the case of histogram matching, it is a representative nonlinear normalization method, which is based on global statistical information. Overall, the radiometric properties are normalized, while the normalization for the phenological properties is not performed at all. That is, when only statistical information is used, it is possible to normalize the radiometric properties, but there is a limit to phenological normalization. RF is a nonlinear method that is performed through RF regression, which uses neighbor information-based features, such as the mean, variance, and gray-level co-occurrence matrix as the training input values [25]. The results of this method show that the radiometric and phenological normalizations are performed, but spectral distortion is present. Furthermore, blurriness occurs in the results due to the neighbor information-based features, which results in some loss of detail, especially on linear objects. On the other hand, our proposed method shows that the radiometric and phenological normalizations are properly performed, and there is no spectral distortion or blurriness in the results. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Number of input variables & 2 \\ Number of hidden layers & 1 \\ Number of neurons in the hidden layer & 3 \\ Number of neurons in the output layer & 1 \\ Activation function & ReLU \\ Loss function & Squared error \\ Optimizer & ADAM \\ Learning rate & \(10^{-4}\) \\ Training epochs & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The hyperparameters of the multilayer perceptron (MLP). Figure 6: Comparison with the results of relative radiometric normalization for site 1: (**a**) proposed method (**b**) MS, (**c**) NC, (**d**) histogram matching, (**e**) RF. Figure 7: Comparison with the results of relative radiometric normalization for site 2: (**a**) proposed method (**b**) MS, (**c**) NC, (**d**) histogram matching, (**e**) RF. To verify the radiometric and phenological normalization results in detail, some regions are selected and enlarged, with red rectangles representing the built-up areas and green rectangles representing the vegetation areas. Figure 9 shows built-up areas, which represent the radiometric properties, consist of roads and the roofs of buildings, and the enlarged areas for each normalization result. For roads, the proposed method normalizes the radiometric properties in such a way that the image is the closest in consistency with the reference image, while, in the case of roofs, the results vary for the site.