## 1 Introduction  
Hyperspectral imaging is a technique that acquires data from many narrow and contiguous spectral bands, enabling spectral signatures of various materials to be detected. Hyperspectral images, HSIs, produce data cubes that consist of a set of two-dimensional images, where each pixel contains the reflectance or radiance values at different wavelengths or bands. HSI classification is a process of assigning each pixel in an HSI to one of several predefined classes, which presents a challenging task due to the high-dimensional nature of hyperspectral data and the complexity of spectral signatures. HSI classification algorithms are designed to extract useful information from the hyperspectral data and map this information to the predefined classes. This process involves the use of mathematical algorithms and statistical techniques to analyse the spectral information contained in each pixel. HSI classification algorithms have a wide range of applications, including mineral and oil exploration and environmental monitoring [1; 2; 3; 4; 5]. In mineral and oil exploration [6], hyperspectral imaging can be used to identify the presence of specific minerals or hydrocarbons based on their unique spectral signatures. Environmental monitoring applications include the detection of pollutants and the mapping of vegetation types [7; 8]. Many algorithms have been developed for HSI classification, including supervised, unsupervised, and hybrid approaches. Supervised algorithms rely on prior knowledge about the spectral properties of the target of interest, and use this information to train a classification model. The most common supervised classification algorithm employed in hyperspectral imaging is the maximum likelihood classifier [9]. This algorithm assumes that the spectral response of each target class is normally distributed, and calculates the probability that each pixel belongs to each class. The pixel is then classified to the class with the highest probability. Other supervised classification algorithms include support vector machines [10], decision trees [11], and artificial neural networks [12]. Unsupervised classification algorithms include clustering algorithms such as k-means [13], hierarchical clustering [14], and self-organizing maps [15; 16]. These algorithms group pixels into clusters based on their spectral similarity, without any prior knowledge of the target classes. The resulting clusters can then be labelled and classified based on their spectral properties. Hybrid algorithms combine the strengths of both supervised and unsupervised approaches. For example, a hybrid algorithm might use an unsupervised clustering algorithm to group pixels into clusters, and then use a supervised algorithm to assign labels to the clusters based on prior knowledge of the target classes. The traditional algorithms mentioned above mostly focus on classifying different extracted features [17]. With the development of deep learning research [18], HSI classification methods have gradually shifted to extracting and classifying high-level deep features. Convolutional neural network (CNN)-based methods are widely used due to their end-to-end architecture and good classification properties. These architectures consist of multiple layers of convolutional and pooling operations, which are used to learn hierarchical features from the input data. The classifier in the end learns to classify each pixel based on the features learned from the input data, and the final output of the algorithm is a classification map that assigns a label or class to each pixel in the HSI. The deep belief network (DBN) [19], stacked autoencoder (SAE) [20], and recurrent neural network (RNN) [21] treat each HSI pixel as independent spectral signatures for deep learning networks, which cannot extract sufficient information. [PERSON] et al. [22] first designed a CNN framework extracting simple deep features from HSIs.