## 1 Introduction  
Hyperspectral imaging (HI), introduced at National Aeronautics and Space Administration (NASA)'s Jet Propulsion Laboratory [1], is a system that consists of spatial in conjunction with spectral coordinates forming a hyperspectral cube. This imaging system is being used in diverse fields viz. security control [2], pharmaceutical analysis [3], agriculture quality [4], biomedical sciences [5], and many others. Hyperspectral remote sensing is an active analysis in the field of the land area [6] and recent examples of remote sensing is an airborne visible/infrared imaging spectrometer (AVIRIS) sensor [7], operational at the NASA Propulsion Laboratory. Nowadays, AVIRIS sensors are capturing more than 200 spectral bands against each pixel, carrying huge information about an object, which is much better than previous primitive sensors [8]. One of the most popular and important areas of processing hyperspectral remote sensing is classification. Here the task is to classify each pixel of the hyperspectral image by assigning a label for generating a thematic land-cover map [9]. In this regard, several studies have been carried out in the last decade by applying a variety of machine learning and image processing methods to extract useful information from hyperspectral data [10]. Hyperspectral image analysis can classify the materials in an area under observation in a better way based on the huge spectral information obtained but at the cost of high spectral dimensionality. On the contrary, high dimensionality brings along with it the curse of the dimensionality problem that destroys classification performance ([PERSON] et al., 2017). Furthermore, the [PERSON] effect arises due to high dimensionality in the hyperspectral data that pose critical problems for supervised learning methods to perform effectively [10; 11], and sometimes one pixel contains a lot of spectral information with spatial information lost. In a similar manner, sometimes, a single channel contains spatial information, but spectral information is lost [12; 13]. Attempts have been made to address the problem, such as that by [PERSON] et al. [14]. They described all the details about spectral-spatial advancements in the scenario of classification. Two kinds of features are mainly extracted from remote sensing data for classification [15]. * First, the main feature is that spectral information lies in the third dimension against each pixel entry in a data cube, and it can be extracted for the exploitation of linear separability of the classes. Numerous methods have been developed for extraction, such as independent component analysis [16], linear spectral unmixing [17; 18], and maximum noise fraction [19]. * The second information is the spatial information for which numerous methods have been used, such as manifold regularization [20; 21], kernel methods [22; 23], and morphological analysis [13; 24]. During the last decade, different conventional machine learning methods have used a large number of training samples for training over data [25; 26]. In remote sensing datasets, normally, a limited number of reliable samples are available. [PERSON] et al. [27] addressed this issue so that all labeled samples are not used in the training process. [PERSON] et al. suggested that prominent samples should be selected only for training, based on active learning (AL), so that the maximum number of samples would be available during the testing phase [28; 29], leading to an agreeable generalization accuracy. [PERSON] et al. [30; 31; 32] have shown that there are different ways to use AL for remote sensing, namely random selection (RS), maximum entropy (ME), mutual information (MI) [33; 34], breaking ties (BT) [35], and modified breaking ties (MBT) [36]. [PERSON] et al. showed a significant improvement in classification with extracted spectral and spatial features from the dataset. Better classification is achieved when reliable samples are being used in training [37].