## 6 Description of the Datasets
#### Metrics Used for the Assessment of the Modeling Effort  
The following metrics were used for the assessment of the modeling effort [33; 34]:  
(a) Overall Accuracy (OA): This index represents the number of samples correctly classified, divided by the number of testing samples.  
(b) Kappa Statistic: This is a statistical measure which provides information on the level of agreement between the truth map and the final classification map. It is the percentage of agreement corrected by the level of agreement, which could be expected to occur by chance. In general, it is considered to be a more robust index than a simple percent agreement calculation, since \(k\) takes into account the agreement occurring by chance. It is a popular measure for benchmarking classification accuracy under class imbalance. It is used in static classification scenarios and for streaming data classification. [PERSON]'s kappa measures the agreement between two raters, where each classifies \(N\) items into \(C\) mutually exclusive categories. The definition of \(\kappa\) is [35; 36]:  
\[\kappa=\frac{p_{0}-p_{e}}{1-p_{e}}=1-\frac{1-p_{0}}{1-p_{e}}, \tag{19}\]  
where \(p_{o}\) is the relative observed agreement among raters (identical to accuracy), and \(p_{e}\) is the hypothetical probability of chance agreement. The observed data are used to calculate the probabilities of each observer, to randomly see each category. If the raters are in complete agreement, then \(\kappa=1\). If there is no agreement among the raters other than what would be expected by chance (as given by \(p_{e}\)), then \(\kappa\approx 0\).  
The Kappa Reliability (KR) can be considered as the outcome from the data editing, allowing the conservancy of more relevant data for the upcoming forecast. A detailed analysis of the KR is presented in the following Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Kappa** & **Reliability** \\ \hline
0.00 & no reliability \\
0.1–0.2 & minimum \\
0.21–0.40 & little \\
0.41–0.60 & moderate \\
0.61–0.80 & important \\ \(\geq\)0.81 & maximum \\ \hline \hline \end{tabular}
\end{table}
Table 4: Kappa Reliability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class No**} & \multirow{2}{*}{**Class Name**} & \multirow{2}{*}{**All Instances**} & \multicolumn{4}{c}{**Sets**} \\ \cline{3-6}  & & & **Training** & **Validation** & **Test** \\ \hline
1 & Asphalt & 816 & \(\surd\) & - & - \\
2 & Meadows & 824 & \(\surd\) & - & - \\
3 & Trees & 820 & \(\surd\) & - & - \\
4 & Bare Soil & 820 & \(\surd\) & - & - \\
5 & Self-Blocking Bricks & 808 & \(\surd\) & - & - \\
6 & Bitumen & 808 & \(\surd\) & - & - \\
7 & Shadows & 476 & \(\surd\) & - & - \\
8 & Water & 824 & - & \(\surd\) & - \\
9 & Tiles & 1260 & - & - & \(\surd\) \\ \hline
**Total** & **7456** & **5372** & **824** & **1260** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pavia Center dataset.  
(c) [PERSON] test: The [PERSON] statistical test was employed to evaluate the importance of classification accuracy derived from different approaches [31]:  
\[z_{12}=\frac{f_{12}-f_{21}}{\sqrt{f_{12}+f_{21}}}, \tag{20}\]  
where \(f_{ij}\) is the number of correctly classified samples in classification \(i\), and incorrect one are in classification \(j\). [PERSON]'s test is based on the standardized normal test statistic, and therefore the null hypothesis, which is \"no significant difference,\" rejected at the widely used \(p=0.05\) (\(|z|>1.96\)) level of significance.