## 2 Literature Survey  
Recent work has thoroughly examined the integration of spatial information for HSI categorization. Prior to classification, one popular method is to generate different spatial feature vectors. Rich spatial-spectral feature vectors, which are then fed into classifiers like SVM, are created by stacking these spatial feature vectors with spectral feature vectors. Based on past knowledge and research goals, users choose the kind of spatial feature generation method and its parameters. Some important spatial features include morphological profiles and their related topics [13; 14; 15], Gabor filters [16; 17], local binary patterns [18; 19], and superpixels [19]. Post-processing is another strategy to enhance HSI classification by incorporating spatial information after classification. For instance, [PERSON] et al. and [PERSON] et al. employed Markov random fields as a post-processing technique to improve the classification performance [20; 21; 22]. [PERSON] et al. proposed a novel post-processing technique based on edge-preserving filters (EPFs), in that an EPF is applied to classified images to incorporate spatial features and reduce noise [23]. [PERSON] et al. developed a strategy combining spatial information from probability maps derived from SVM classifiers with EPFs iteratively to enhance the classification performance [24; 25]. In addition to the mentioned methods that consider the spatial information in the pre-process or post-processing of the classification procedure, some recent classification approaches integrate spatial information innovatively during the classification process. In order to properly exploit spatial-spectral information, [PERSON] et al., for instance, developed a spatially aware collaborative representation approach that directly includes spatial information through a spatial regularization term in the representation objective function [26]. [PERSON] et al. offered a unique low-rank representation model that is regularized by both locality and structure [27]. It incorporates a new distance metric that combines spectral and spatial characteristics and a structure constraint to increase the classification accuracy. [PERSON] et al. provided a brand-new spatial peak-aware collaborative representation technique for HSI classification that uses superpixel clusters' spectral-spatial data to input regularization terms for a brand-new closed-form solution [28]. Deep learning has attracted much interest in HSI analysis, in addition to conventional techniques, because of its innate capacity to take spatial pixel properties into account during categorization. Several deep-learning strategies have been put forth to improve the HSI classification accuracy using CNNs [29; 30]. For example, ensemble deep-learning methods utilize local covariance descriptors from different window sizes around each pixel as inputs to a CNN classifier [31]. Another method proposed a new deep feature extraction method, named a random patches network, to extract deep features, and then the SVM classifier was fed with shallow and deep features to classify HSI [32]. For few-shot learning tasks, a multidimensional CNN model with convolution operators and an attention mechanism has also been utilized [33]. For HSI classification, a novel deep-learning model based on bidirectional long short-term memory (LSTM) and 3D convolutions has been presented [34]. To enhance the CNN performance, a hybrid deep-learning technique known as an attention-fused hybrid network combines an attention mechanism with a 3D and 2D CNN inception net [35]. Another approach involves first extracting spatial and spectral features and then using them as input to a CNN classifier [36]. A ghost module-based spectral network utilizing Ghost3D and Ghost2D modules, along with non-local operations, achieves superior performance in HSI classification [37]. A multi-feature fusion algorithm combines 3 DCNN and a graph attention network to enhance the classification accuracy in HSI [38].