## 1 Introduction  
Due to advancements in remote sensing image acquisition mechanisms and the increasing availability of rich spatial and spectral data by means of various sensors, hyperspectral imaging has become more prominent [1]. Especially, hyperspectral remote sensing image (HSI) classification has become a major source for real-time application in fields such as mineral mapping, agriculture, environment, and forestry, etc. [2; 3]. Usually, the HIS is taken at a large number of contiguous narrow spectral wavelengths for the improved analysis of the earth object. Since the spectral resolution could be in nm, the hyperspectral sensor offers significant facility in data analysis [4] for many humanitarian tasks, including precision agriculture for improved farming practices, discrimination amongst vegetation classes for better treatment, etc. [5]. The current study emphasizes using and analyzing HSI in the agriculture area. Conventional techniques, such as statistical-based analyses and field surveys, are time-consuming [6]. Cutting-edge remote sensing technologies involving HSI provide an appropriate solution and might fill the gap with solutions such as crop classification. In the HSI framework, the classification has the common objective of automatically labeling the pixel (spectral pattern or signature) into a predetermined class [7]. The classification is implemented either by utilizing the transformed feature or the original feature. An HSI has numerous features and is hard to adapt to a single convolutional kernel size. When the number of model layers is increased, many useful features are lost [8; 9; 10]. The authors of [11] proposed a rotation-invariant local binary pattern-based weighted generalized closest neighbor (RILBP-WGCN) approach for an HSI classifier. The presented RILBP is an improved texture-based classifier paradigm, which employs LBP filters to any designated bands to generate a wide sketch of spatial texture data. Similarly, the presented WGCN approach effectually maintained the spatial uniformity amongst the adjacent pixel employing a local weight method and point-to-set distances. [PERSON] et al. [12] concentrated on a DL-based crop mapping, utilizing one-shot hyperspectral satellite imagery, whereas three CNN techniques, such as 1D-CNN, 2D-CNN, and 3D-CNN, were executed for end-to-end crop mapping. Furthermore, a manifold learning-based visualized method, i.e., t-distributed stochastic neighbor embedding (t-SNE), was established for demonstrating the discriminative capability of deep semantic feature extracting by the distinct CNN approaches. In [13], a hybrid model was established for estimating the chlorophyll content from the crops utilizing HIS segmentation with active learning, which contains two important stages. First, it can utilize a sparse multinomial logistic regression (SMLR) method for learning the class posterior probability distribution with quadratic programming or joint probability distributions. Second, it can utilize the data developed from the preceding step for segmenting the HSI utilizing a Markov random field segment. [PERSON] et al. [14] examine patch-based weed identification utilizing HSI. A CNN was estimated and correlated to a histogram of oriented gradients (HoG) for this solution. Appropriate patch sizes were examined. The restriction of RGB imagery was established. In [15], a deep one-class crop (DOCC) structure that contains a DOCC extracting element and an OCC extraction loss element was presented for large-scale OCC mapping. The DOCC structure takes only the instances of one target class as input for extracting the crop of interest by positive and unlabeled learning and automatically extracts the feature for OCC mapping. In [16], a low altitude UAV hyperspectral remote sensing platform was created for collecting higher spatial resolution remote sensing images of degraded grassland.