## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Training**} \\ \hline Class & 1 & 2 & 3 & 4 & Total & User’s (\%) \\ \hline
1 & 398 & 40 & 2 & 0 & 440 & 90.45 \\
2 & 2 & 360 & 0 & 0 & 362 & 99.44 \\
3 & 0 & 0 & 398 & 0 & 398 & 100 \\
4 & 0 & 0 & 0 & 400 & 400 & 100 \\ \hline Total & 400 & 400 & 400 & 400 & 1600 & \\ \hline Producer’s (\%) & 99.50 & 90.00 & 99.50 & 100 & & \\ \hline \multicolumn{6}{c}{**Validation**} \\ \hline Class & 1 & 2 & 3 & 4 & Total & User’s (\%) \\ \hline
1 & 490 & 9 & 0 & 0 & 499 & 98.19 \\
2 & 10 & 91 & 0 & 0 & 101 & 90.09 \\
4 & 0 & 0 & 0 & 100 & 100 & 100 \\ \hline Total & 500 & 100 & 100 & 100 & 800 & \\ \hline \multicolumn{6}{c}{**Testing**} \\ \hline Class & 1 & 2 & 3 & 4 & Total & User’s (\%) \\ \hline
1 & 493 & 6 & 0 & 0 & 499 & 98.79 \\
2 & 7 & 94 & 0 & 0 & 101 & 93.06 \\
3 & 0 & 0 & 100 & 0 & 100 & 100 \\
4 & 0 & 0 & 0 & 100 & 100 & 100 \\ \hline Total & 500 & 100 & 100 & 100 & 800 & \\ \hline Producer’s (\%) & 98.60 & 94.00 & 100 & 100 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrices for the network selected using a validation set formed by random sampling. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Validation Dataset**} & \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Algorithm and Iterations**} & \multicolumn{2}{c}{**Classification Accuracy (\%) Training Validation Testing**} \\ \hline Random & 9:11:6 & Conjugate gradient, 435 & 97.27 & 98.18 & 97.87 \\ Stratified & 11:16:6 & Conjugate gradient, 205 & 96.21 & 97.27 & 96.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Key characteristics of the networks selected for the analyses of the remotely-sensed data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**(a) Training**} \\ \hline
**Class** & **1** & **2** & **3** & **4** & **Total** & **User’s (\%)** \\ \hline
1 & 386 & 14 & 0 & 0 & 400 & 96.50 \\
2 & 14 & 386 & 0 & 0 & 400 & 96.50 \\
3 & 0 & 0 & 400 & 0 & 400 & 100 \\
4 & 0 & 0 & 0 & 400 & 400 & 100 \\ \hline Total & 400 & 400 & 400 & 400 & 1600 & \\ \hline Producer’s (\%) & 96.50 & 96.50 & 100 & 100 & & \\ \hline \multicolumn{1}{c}{**(b) Validation**} & & & & & \\ \hline
**Class** & **1** & **2** & **3** & **4** & **Total** & **User’s (\%)** \\ \hline
1 & 195 & 5 & 0 & 0 & 200 & 96.50 \\
2 & 5 & 195 & 0 & 0 & 200 & 96.50 \\
3 & 0 & 0 & 200 & 0 & 200 & 100 \\
4 & 0 & 0 & 0 & 200 & 200 & 100 \\ \hline Total & 200 & 200 & 200 & 200 & 800 & \\ \hline Producer’s (\%) & 97.50 & 97.50 & 100 & 100 & & \\ \hline \multicolumn{1}{c}{**(c) Testing**} & & & & \\ \hline
**Class** & **1** & **2** & **3** & **4** & **Total** & **User’s (\%)** \\ \hline
1 & 473 & 0 & 0 & 0 & 473 & 100 \\
2 & 27 & 100 & 0 & 0 & 127 & 78.74 \\
3 & 0 & 0 & 100 & 0 & 100 & 100 \\
4 & 0 & 0 & 0 & 100 & 100 & 100 \\ \hline Total & 500 & 100 & 100 & 100 & 800 & \\ \hline Producer’s (\%) & 94.60 & 100 & 100 & 100 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Confusion matrices for the network selected using a validation set formed by stratified sampling. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Correct** & **Incorrect** & **Total** \\ \hline Correct & 767 & 6 & 773 \\ Incorrect & 20 & 7 & 27 \\ \hline Total & 787 & 13 & 800 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Cross-tabulation of labelling from classifiers using random (columns) and stratified (rows) validation sets for the simulated data. the same testing set, revealed it to be statistically significant at the 95% level of confidence (Table 13). Specifically, given the seven discordant cases observed (Table 13), Equation (4) yields \(z=1.889\). The difference in accuracy between the classifications of the testing sets by the two selected neural networks (Tables 11 and 12) was attributable mostly to seven cases of sugar beet being commissioned into the potato class when the validation set formed using a stratified sample was used.