## 3 Results and Discussion
### Experimental Setup
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class No.**} & \multirow{2}{*}{**Class Name**} & \multicolumn{4}{c}{**Segmentation Accuracy over Different AL Methods**} \\ \cline{3-6}  & & \multirow{2}{*}{**Random**} & \multirow{2}{*}{**Maximum Entropy**} & \multirow{2}{*}{**Mutual Information**} & \multirow{2}{*}{**Breaking Ties**} & \multicolumn{2}{c}{**Modified Breaking Ties**} \\ \cline{3-6}  & & & & **Random** & **Maximum** & **Mutual Information** & **Ties** & **Modified Ties** \\ \hline
1 & Alfalfa & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
2 & Corn-no till & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
3 & Corn-min till & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
4 & Corn & 99.84 & 100.00 & 99.56 & 100.00 & 100.00 \\ \hline
5 & Grass-pasture & 94.16 & 92.50 & 84.50 & 93.46 & 99.63 \\ \hline
6 & Grass-tree & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
7 & Grass-pasture-mowed & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
8 & Hay-windrowed & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
9 & Oats & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
10 & Soyabeans-no till & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
11 & Soyabeans-min till & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
12 & Soyabeans-clean till & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
13 & Wheat & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
14 & Woods & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
15 &
\begin{tabular}{c} Building-grass- \\ \(\text{drives}\) \\ \end{tabular} & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
16 &
\begin{tabular}{c} Stone-steel \\ \(\text{towers}\) \\ \end{tabular} & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline  & Overall Accuracy & 99.44 & 99.23 & 98.50 & 99.59 & 99.98 \\ \hline  & Average Accuracy & 99.62 & 99.51 & 99.00 & 99.59 & 99.98 \\ \hline  & Kappa Statistics & 99.21 & 98.93 & 97.19 & 99.43 & 99.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Segmentation accuracy of the Indian Pines dataset (class-wise) using HLS. In the Pavia University experiment, the pictorial representation of ground truth, classification and segmentation landscapes of our proposed methodology are shown in Figure 4a-c. The representation of nine classes, i.e., asphalt, bare soil, bitumen, bricks, gravel, meadows, metal sheets, shadows, and trees, have been used to produce the ground truth. Every class with separate color can be seen after the application of the algorithm. Similar to the previous experimentation, empirically found number of samples using different active learning algorithms are computed by ten independent Monte Carlo runs. The classification results of each class are shown in Table 4 as OA, AA, and k-statistics as 99.14, 98.56, and 99.01%, respectively. The segmentation results in tabular form are illustrated in Table 5 for different active learning strategies. The segmentation results of the proposed methodology for the Pavia University dataset are: (OA, AA, and k-statistics as 99.42, 99.29, and 99.28%, respectively). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sr. No.**} & \multirow{2}{*}{**References**} & \multicolumn{3}{c}{**Classification Accuracy**} & \multicolumn{3}{c}{**Segmentation Accuracy**} \\ \cline{3-8}  & & **OA** & **AA** & **Kappa** & **OA** & **AA** & **Kappa** \\ \hline
1 & [PERSON] et al. [28] & 19.08 & 11.64 & 6.66 & 14.37 & 17.73 & 12.34 \\ \hline
2 & [PERSON] et al. [30] & 38.32 & 48.30 & 40.04 & 42.05 & 62.41 & 39.04 \\ \hline
3 & [PERSON] et al. [31] & 55.55 & 64.32 & 50.04 & 62.49 & 70.92 & 57.75 \\ \hline
4 & [PERSON] et al. [32] & 48.32 & 64.38 & 44.19 & 82.84 & 88.32 & 78.19 \\ \hline
5 & Proposed (HLS) & 99.93 & 99.86 & 99.84 & 99.98 & 99.98 & 99.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of classification and segmentation accuracies of the Indian Pines dataset (class-wise) on 10 repetitions using 5 training samples of each class.