## 1 Introduction  
Hyperspectral imaging (HSI) examines the reflection of light of an object across a wide range of electromagnetic spectra instead of just associating primary colors to a pixel [1]. Light interacting with a pixel is divided into several bands in order to render complete information about a target [2]. Therefore, HSI has gained significant importance in many applications including. but not limited to,chemical imaging [3], agriculture [4], surveillance [5], remote sensing [6], household materials [7], and environmental sciences [8]. The main challenge of HSI analysis is its high dimensional characteristic of data due to a large number of bands with significantly high resolution across the electromagnetic spectrum. Therefore, the classification of HSI data is a complex and challenging task [9]. Some widely used supervised classification approaches for HSI analysis are Multinomial Logistic Regression (MLR) [10], Support Vector Machine (SVM) [11], Maximum Likelihood [10; 12], Ensemble Learning (EL) [13; 14], Random Forests (RF) [15; 16], Deep Learning (DL) [17; 18; 19], Transfer Learning [20; 21], k-Nearest Neighbors (KNN) [22] and Extreme Learning Machine (ELM) [23]. The major limitation of supervised HSI classification is the poor performance due to the Hughes phenomena [24]. It occurs when the ratio of spectral bands is significantly less as compared to the labeled training samples available in hyperspectral data [25]. The acquisition of most informative labeled training examples is often an expensive and time-intensive task as it generally requires human experts or a ground campaign [26]. Limited availability of reliable labeled training examples brings the idea to utilize semi-supervised learning [27]. The basic concept of such a learning mechanism is that new training examples can be obtained from the unlabeled data, without considerable time and cost, by utilizing limited available labeled examples [28; 29]. A few examples of such techniques are kernel techniques [30], such as EL techniques [31; 32], Tri-training [33; 34] and Graph-based learning [35]. However, the performance of these techniques is relatively low when combined with the limited availability of reliable labeled training examples for high dimensional datasets e.g., hyperspectral datasets. To cope with the aforesaid issues, one of the commonly used semi-supervised approaches is the expansion of the initial training set by efficiently utilizing unlabeled data. This method is known as Active Learning (AL) which significantly improves the performance of classification techniques by adding new examples in the training set for the next cycle of training, unless a stopping criteria is met, i.e., required classification accuracy. Depending on the criteria of adding new examples to the training set, AL techniques can be categorized as stream or pool-based and the selection of new examples is based on ranking scores that are reckoned from measures like representativeness, uncertainty, variance, inconsistency and error [6]. For instance, uncertainty measures consider the unlabeled examples more important than those close to the class boundary of the current classification results. Representativeness selects those unlabeled examples as more significant, which can represent a new group of examples, i.e., a cluster. Inconsistency assumes the unlabeled samples more useful and has high predictive divergence among multiple classifiers [36]. However, all these methods have one major limitation which is randomness among the samples. The new training samples are always selected with certain criteria without considering whether the selected samples are similar to the previously selected samples or not which induces the redundancy among the samples. The other main issue with many AL methods is their stopping criteria which are most certainly based on the accuracy number.