## 3 Results and Discussion
### Experimental Setup
Figure 4: (**a**) Ground truth map (dimensions: 610 \(\times\) 340), (**b**) classification map for the Pavia University dataset using HLS (dimensions: 610 \(\times\) 340), and (**c**) segmentation map for the Pavia University dataset using HLS (dimensions: 610 \(\times\) 340). The existing models [28, 30, 31, 32] were tested on freely available datasets, and further, the results were compared with the proposed framework (Tables 3 and 6) using five samples from each class (Tables 1 and 4). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class No.**} & \multirow{2}{*}{**Class Name**} & \multicolumn{4}{c}{**\# Samples**} & \multicolumn{4}{c}{**Classification Accuracy over Different AL Methods**} \\ \cline{3-8}  & & **Train** & **Test** & \begin{tabular}{c} **Random** \\ **Selection** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Entropy** \\ \end{tabular} & \begin{tabular}{c} **Mutual** \\ **Information** \\ \end{tabular} & \
\begin{tabular}{c} **Breaking** \\ **Ties** \\ \end{tabular} &
\begin{tabular}{c} **Modified** \\ **Breaking** \\ **Breaking** \\ \end{tabular} \\ \hline
1 & Asphalt & 5 & 7174 & 98.28 & 97.81 & 96.52 & 99.13 & 99.01 \\ \hline
2 & Bare Soil & 5 & 19,184 & 99.88 & 99.99 & 100 & 99.98 & 99.98 \\ \hline
3 & Bitumen & 5 & 2486 & 96.68 & 95.63 & 93.04 & 98.27 & 98.05 \\ \hline
4 & Bricks & 5 & 3583 & 99.96 & 99.99 & 100.00 & 100.00 & 100.00 \\ \hline
5 & Gravel & 5 & 1605 & 99.99 & 100.00 & 100.00 & 100.00 & 99.99 \\ \hline
6 & Meadows & 5 & 5556 & 83.60 & 88.87 & 96.05 & 89.92 & 90.03 \\ \hline
7 & Metal Sheets & 5 & 1700 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
8 & Shadows & 5 & 4191 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
9 & Trees & 5 & 1173 & 99.98 & 99.98 & 99.65 & 99.48 & 99.99 \\ \hline  & Overall Accuracy & & 99.09 & 99.33 & 99.25 & 99.37 & 99.14 \\ \hline  & Average Accuracy & & 97.59 & 98.03 & 98.36 & 98.53 & 98.56 \\ \hline  & Kappa Statistics & & 98.23 & 98.72 & 98.93 & 99.09 & 99.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy of the Pavia University dataset (class-wise) on 10 independent repetitions using HLS. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class No.**} & \multirow{2}{*}{\begin{tabular}{c} **Class** \\ **Name** \\ \end{tabular} } & \multicolumn{4}{c}{**Segmentation Accuracy over Different AL Methods**} \\ \cline{3-8}  & & \begin{tabular}{c} **Random** \\ **Selection** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Entropy** \\ \end{tabular} & \begin{tabular}{c} **Mutual** \\ **formation** \\ \end{tabular} & \
\begin{tabular}{c} **Breaking** \\ **Ties** \\ \end{tabular} &
\begin{tabular}{c} **Modified** \\ **Breaking** \\ **Breaking** **Ties** \\ \end{tabular} \\ \hline
1 & Asphalt & 98.97 & 98.95 & 98.48 & 99.75 & 99.64 \\ \hline
2 & Bare Soil & 99.75 & 99.92 & 99.88 & 99.87 & 99.85 \\ \hline
3 & Bitumen & 98.19 & 97.98 & 97.09 & 99.62 & 99.44 \\ \hline
4 & Bricks & 99.99 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
5 & Gravel & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
6 & Meadows & 86.74 & 92.95 & 97.26 & 94.72 & 94.90 \\ \hline
7 &
\begin{tabular}{c} Metal \\ sheets \\ \end{tabular} & 100.00 & 99.99 & 99.99 & 99.99 & 99.84 \\ \hline
8 & Shadows & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
9 & Trees & 100.00 & 100.00 & 100.00 & 100.00 & 99.99 \\ \hline Overall Accuracy & 99.34 & 99.59 & 99.55 & 99.69 & 99.42 \\ \hline Average Accuracy & 98.18 & 98.87 & 99.19 & 99.33 & 99.29 \\ \hline Kappa Statistics & 98.45 & 99.21 & 99.32 & 99.48 & 99.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Segmentation accuracy of the Pavia University dataset (class-wise) using HLS.