## 3 Experimental Results
### Spectral Radiance Pattern Analysis of Pothole Surface from UAV Calibrated Bands  
In this study, the presence of potholes was predicted by training the MLP with sigmoid calibration function on various spectral channels of the UAV imagery. Table 3 provides detailed information regarding the parameters used to train MLP with sigmoid calibration function.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & & **Blue Channel** & **Green Channel** & **Red Channel** & **NIR Channel** & **Red Edge Channel** \\ \hline \multirow{4}{*}{Input specifications} & Independent image & B, G, R, N, & B, G, Red, NIR, & B, G, R, N, & B, G, Red, NIR, & B, G, R, N, \\  & R\_edge & R\_edge & R\_edge & R\_edge & R\_edge & R\_edge \\ \cline{1-1}  & Dependent variable images & Blue channel & Green channel & Red channel & NIR channel & Red edge \\ \cline{1-1}  & Training mask & Boolean image & Boolean image & Boolean image & Boolean image & Boolean image \\ \cline{1-1}  & Max. training & 500 & 500 & 500 & 500 & 500 \\ \cline{1-1}  & pixels used & 500 & 500 & 500 & 500 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Learning parameters for sigmoid calibration function-trained MLP.  
Figure 5: Road pothole features demarcated on original spectral channels: (**a**) blue, (**b**) green, (**c**) red, (**d**) NIR and (**e**) red edge channels.  
The sigmoid calibration model neural network was applied across several spectral channels of UAV imagery, with a view to determine the spectral channel on which the sigmoid calibration function provides the best pothole estimate. Table 4 shows the descriptive statistics generated from the radiance samples obtained from calibrated spectral channels.  
Based on the descriptive statistics presented in Table 3, the potholes were also demarcated based on the analysis of their upper and lower radiance threshold values. Figure 6 shows potholes as demarcated on each calibrated spectral channel.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Calibrated Spectral Bands** & **N** & **Min** & **Max** & \(\mu\) & \(\sigma\) & \(p\)**-Value** \\ \hline Blue & 25 & 0.848 & 0.911 & 0.868 & 0.032 & 0.004 \\ Green & 25 & 0.783 & 0.937 & 0.886 & 0.021 & 0.001 \\ Red & 25 & 0.925 & 0.961 & 0.944 & 0.011 & 0.002 \\ NIR & 25 & 0.18 & 0.32 & 0.211 & 0.024 & 0.018 \\ Red edge & 25 & 0.311 & 0.906 & 0.863 & 0.066 & 0.014 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive statistics of radiance samples obtained from calibrated spectral channels.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & & **Blue Channel** & **Green Channel** & **Red Channel** & **NIR Channel** &
\begin{tabular}{l} **Red Edge** \\ **Channel** \\ \end{tabular} \\ \hline Network topology & No. of input & 5 & 5 & 5 & 5 & 5 \\  & layer nodes & 1 & 1 & 1 & 1 & 1 \\  & No. of hidden layers & 1 & 1 & 1 & 1 & 1 \\  & Layer 1 nodes & 5 & 5 & 5 & 5 & 5 \\ \hline Training parameters & Training type & Automatic & Automatic & Automatic & Automatic & Automatic \\  & Learning rate type & Dynamic & Dynamic & Dynamic & Dynamic & Dynamic \\ Training parameters & Learning rate start & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 \\  & Learning rate end & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 \\  & Momentum factor & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\  & Sigmoid constant & 1 & 1 & 1 & 1 & 1 \\ \hline  & Iteration & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 \\  & Actual training pixels & 490 & 497 & 474 & 483 & 496 \\  & Actual & 491 & 497 & 475 & 483 & 497 \\ Stopping criteria & testing pixels & 0.0010 & 0.0010 & 0.0010 & 0.0010 & 0.0010 \\  & RMS & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 \\  & Training RMS & 0.0322 & 0.0334 & 0.0345 & 0.0185 & 0.0349 \\  & Testing RMS & 0.0344 & 0.0365 & 0.0325 & 0.0196 & 0.0350 \\  & R.sqr & 0.942 & 0.987 & 0.772 & 0.979 & 0.986 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._