## 3 Results and Discussion
### Experimental Setup
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class No.**} & \multirow{2}{*}{**Class Name**} & \multicolumn{3}{c}{**\#Samples**} & \multicolumn{3}{c}{**Classification Accuracy over Different AL Methods**} \\ \cline{3-10}  & & & **Train** & **Test** & \begin{tabular}{c} **Random** \\ **Selection** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Entropy** \\ \end{tabular} & \begin{tabular}{c} **Mutual In-** \\ **formation** \\ \end{tabular} & \
\begin{tabular}{c} **Breaking** \\ **Ties** \\ \end{tabular} &
\begin{tabular}{c} **Modified** \\ **Breaking Ties** \\ \end{tabular} \\ \hline
1 & Alfafla & 5 & 50 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
2 & Corn-no till & 5 & 1429 & 99.90 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
3 & Corn-min till & 5 & 829 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
4 & Corn & 5 & 229 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
5 & Grass-pasture & 5 & 492 & 93.54 & 90.38 & 82.06 & 90.37 & 97.87 \\ \hline
6 & Grass-tree & 5 & 742 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification accuracy of Indian Pines dataset (class-wise) on 10 independent repetitions using active learning techniques. Figure 3: (**a**) Ground truth map (dimensions: 145 \(\times\) 145), (**b**) classification map for the Indian Pines dataset using HLS (dimensions: 145 \(\times\) 145), and (**c**) segmentation map for the Indian Pines dataset using HLS (dimensions: 145 \(\times\) 145). \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{3-10}  & & \multirow{2}{*}{**Train**} & \multirow{2}{*}{**Test**} & \multicolumn{2}{c}{**Random**} & \multirow{2}{*}{**Meximum**} & \multicolumn{2}{c}{**Mutual Information**} & \multirow{2}{*}{**Breaking Ties**} & \multicolumn{2}{c}{**Modified Ties**} \\ \cline{3-10}  & & & & **Selection** & & **Entropy** & **Methual Information** & **Ties** & **Modified Ties** \\ \hline \hline
7 &
\begin{tabular}{c} Grass-pasture- \\ \(\text{mowed}\) \\ \end{tabular} & 5 & 21 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
8 & Hay-windrowed & 5 & 484 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
9 & Oats & 5 & 15 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
10 & Soyabeans-no till & 5 & 961 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
11 &
\begin{tabular}{c} Soyabeans-min \\ \(\text{till}\) \\ \end{tabular} & 5 & 2463 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
12 &
\begin{tabular}{c} Soyabeans-clean \\ \(\text{till}\) \\ \end{tabular} & 5 & 609 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
13 & Wheat & 5 & 207 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
14 & Woods & 5 & 1289 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
15 &
\begin{tabular}{c} Building-grass- \\ \(\text{trees-drives}\) \\ \end{tabular} & 5 & 375 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
16 &
\begin{tabular}{c} Stone-steel \\ \(\text{towers}\) \\ \end{tabular} & 5 & 90 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline  & Overall Accuracy & & 99.92 & 99.39 & 98.13 & 99.27 & 99.93 \\ \hline  & Average Accuracy & & 99.59 & 99.39 & 98.87 & 99.39 & 99.86 \\ \hline  & Kappa Statistics & & 98.99 & 99.77 & 97.38 & 98.96 & 99.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._