## 3 Experimental Process  
The performance of our proposed pipeline is validated on 3 benchmark HSI datasets that are publicly available and acquired by two different sensors, i.e., Reflective Optics System Imaging Spectrometer (ROSIS) and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. These 3 datasets are Salinas, Indian Pines and Pavia University (PU). More information on these datasets can be found in [9; 44]. Furthermore, we evaluated our proposed pipeline against five diverse classifiers which are Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Ensemble learning (EL) methods i.e., Gradient Boost (GB) & Logistic Boost (LB) and Extreme Learning Machine (ELM) [45]. We choose these classifiers for comparison purposes because they have been broadly studied for HSI classification. The performance of our proposed pipeline and the aforementioned classifiers is evaluated using two benchmark metrics: Overall Accuracy (OA) and kappa (\(\kappa\)) coefficient [25]. Furthermore, to check the statistical significance of our proposed pipeline, several statistical measures are taken into accounts, such as Precision, Recall and F1-score. All these metrics are computed as follows:  
\[OA=\frac{1}{C}\sum_{i=1}^{C}TP_{i} \tag{6}\]\[Precision=\frac{1}{C}\sum_{i=1}^{C}\frac{TP_{i}}{TP_{i}+FP_{i}} \tag{7}\]  
\[Recall=\frac{1}{C}\sum_{i=1}^{C}\frac{TP_{i}}{TP_{i}+FN_{i}} \tag{8}\]  
\[F1-Score=\frac{2\times(Recall\times Precision)}{(Recall+Precision)} \tag{9}\]  
\[\kappa=\frac{P_{o}-P_{e}}{1-P_{e}} \tag{10}\]  
where  
\[P_{o}=\frac{TP+TN}{TP+FN+FP+TN} \tag{11}\]  
\[P_{e}=P_{Y}+P_{N} \tag{12}\]  
\[P_{Y}=\frac{TP+FN}{TP+FN+FP+TN}\times\frac{TP+FN}{TP+FN+FP+TN} \tag{13}\]  
\[P_{N}=\frac{FP+TN}{TP+FN+FP+TN}\times\frac{FN+TN}{TP+FN+FP+TN} \tag{14}\]  
where TP, FP, TN and FN are true positive, false positive, true negative and false negative computed from the confusion matrix, respectively. Moreover, to show that our proposed pipeline is commensurable, we evaluated it against four well-known sample selection methods: Mutual Information (MI), Breaking Ties (BT) and Modified Breaking Ties (MBT). 1. **Mutual Information (MI):** Selects the samples by maximizing the mutual information between the classifier and class labels and can obtain samples from the complicated region [39]. 2. **Breaking Ties (BT):** Selects the samples by minimizing the distance of the two classes having the highest posterior probabilities, and can choose samples from the boundary regions [46]. In the multiclass scenario, BT can be utilized by finding the difference between the first two most probable classes. 3. **Modified Breaking Ties (MBT):** Adds more diverse samples as compared to MI and BT. The MBT algorithm follows two important steps: first, it selects samples from the unlabeled pool with the same maximum a posteriori (MAP) estimation; and then choose the samples from the most complicated region [46]. In all experiments, 50 samples were selected from the whole HSI data for the initial training dataset and in each iteration of AL process, \(h=50\) samples are actively added to the training dataset. The process is repeated until we achieve the desired accuracy of \(\geq\)85% for at least one classifier or training set size reaches up to \(h=2500\) samples. SVM is evaluated with polynomial kernel function and in the case of ELM, [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 500] hidden neurons are selected. Similarly, tree-based methods are used to train the ensemble classifiers from a range of [1; 2; 3; 4; 5; 6; 7; 8; 9; 100] trees and kNN parameter \(k\) is set to [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. All the parameters are adjusted carefully while setting up the experiments.