## 3 Results and Discussion
As a result of these errors the producer's accuracy for the sugar beet class declined from 99.00% when the validation sample acquired with a random sample was used to 93.00% when the validation sample had been generated with a stratified sample. The user's accuracy for the potato class also differed for the classifications of the testing set obtained when using the validation set defined with random and stratified sampling, the accuracies being 96.00% and 77.42%, respectively. Given that the testing set had been generated using a simple random sample design the variation in the number of cases per-class reflected the relative abundance of the classes in the region to be mapped. Critically, the size of the sample of cases for the potato class was approximately one quarter of that for the sugar beet class. As hypothesized, the overall accuracy of the testing set decreased when the stratified rather than random sample was used in validation because cases of an abundant class (sugar beet) were commissioned by a relatively rare class (potato). Thus, even in a situation such as that encountered here, in which the classes are very highly separable, the sample design used in the formation of the validation dataset can have a statistically significant effect on the overall accuracy of the final land cover map, as reflected in the accuracy of the classifications of the testing set (Tables 11 and 12). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Correct** & **Incorrect** & **Total** \\ \hline Correct & 317 & 1 & 318 \\ Incorrect & 6 & 6 & 12 \\ \hline Total & 323 & 7 & 330 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Cross-tabulation of labelling from classifiers using random (columns) and stratified (rows) validation sets for the remotely sensed data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **S** & **W** & **B** & **C** & **P** & **G** & **Total** & **User’s (\%)** \\ \hline S & 99 & 0 & 1 & 0 & 0 & 0 & 100 & 99.00 \\ W & 0 & 96 & 0 & 0 & 1 & 0 & 97 & 98.97 \\ B & 0 & 2 & 52 & 0 & 0 & 0 & 54 & 96.30 \\ C & 0 & 0 & 0 & 34 & 1 & 0 & 35 & 97.14 \\ P & 1 & 0 & 0 & 0 & 24 & 0 & 25 & 96.00 \\ G & 0 & 1 & 0 & 0 & 0 & 18 & 19 & 94.74 \\ \hline Total & 100 & 99 & 53 & 34 & 26 & 18 & 330 \\ \hline Producer’s (\%) & 99.00 & 96.97 & 98.11 & 100 & 92.31 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Confusion matrix for the testing set from the network selected using a validation set formed by random sampling. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **S** & **W** & **B** & **C** & **P** & **G** & **Total** & **User’s (\%)** \\ \hline S & 99 & 0 & 1 & 0 & 0 & 0 & 100 & 99.00 \\ W & 0 & 96 & 0 & 0 & 1 & 0 & 97 & 98.97 \\ B & 0 & 2 & 52 & 0 & 0 & 0 & 54 & 96.30 \\ B & 0 & 2 & 52 & 0 & 0 & 0 & 54 & 96.30 \\ C & 0 & 0 & 0 & 34 & 1 & 0 & 35 & 97.14 \\ P & 1 & 0 & 0 & 0 & 24 & 0 & 25 & 96.00 \\ G & 0 & 1 & 0 & 0 & 0 & 18 & 19 & 94.74 \\ \hline Total & 100 & 99 & 53 & 34 & 26 & 18 & 330 \\ \hline Producer’s (\%) & 93.00 & 97.98 & 98.11 & 100 & 92.31 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Confusion matrix for the testing set from the network selected using a validation set formed by stratified sampling. Finally, it was also evident that the networks selected for the analyses of the remotely-sensed data differed, most notably in terms of architecture (Table 10). When the validation set constructed with simple random sampling had been used, the data acquired in wavebands 1 and 8 were deemed unnecessary and, hence, only nine input units used. In addition this latter network also had fewer hidden units than the network selected when the validation set had been formed with a stratified sample. Overall, the network formed with the use of the validation sample acquired by random sampling was smaller and less complex than that selected when the validation set formed with a stratified sample was used.