## 1 Introduction
[PERSON] developed an information measure-based feature band selection algorithm using fuzzy rough set theory for hyperspectral image classification of soybean varieties [16]. The author used the Gaussian membership function to find the important feature spectra sets and the approach achieved an average accuracy of 99.11%. [PERSON] developed a two-phase approach for hyperspectral image classification [17]. In the first phase, the author utilized a combination of SVM and self-organizing maps for spectral optimization. The optimized spectral set generated from this process was then used in the second phase for classification. This two-step approach enhances the precision of spectral classification by first refining the feature set and then applying a probabilistic method to differentiate between the regions of interest in the hyperspectral image. [PERSON] developed a reinforcement learning-based framework for hyperspectral spectral selection in soil organic matter prediction [18]. This framework addresses the limitations of existing methods by utilizing the Markov decision process and reinforcement learning agents to simulate the selection process and improve model performance. [PERSON] and [PERSON] developed an SVM accuracy-based spectral band selection algorithm [19]. The SVM classification accuracy varied with spectra count, especially for small datasets. [PERSON] developed a neighborhood mutual information-based minimum Redundancy Maximum Relevance (mRMR) spectral band selection algorithm [20]. [PERSON] developed a Salp Swarm Algorithm for Feature Selection (SSAFS) for image-based plant disease detection [21]. SSAFS aimed to identify the optimal subset of features that maximizes classification accuracy while minimizing the number of features used. [PERSON] further developed a hybrid feature selection algorithm, to improve HSI classification [22]. This method combined feature extraction and selection techniques. [PERSON] described a novel deep-learning based approach for HSI-based seed variety identification [23]. [PERSON] developed a Modified Ant Lion Optimizer (MALO) spectral selection algorithm [24]. MALO combines the Ant Lion optimizer swarm intelligence algorithm with a wavelet support vector machine to address the combinatorial optimization problem of feature selection. The multilayer perception (MLP) network can be used to find important feature spectra in a high-dimensional dataset [25; 26]. While MLP can achieve higher accuracy, it often lacks interpretability [27]. [PERSON] used single-layer MLP to find the important features band [28]. The authors used network weight to find the feature's importance score from the trained network. [PERSON] and [PERSON] used an MLP network to train and classify various data and sequentially remove irrelevant features based on certain criteria [29]. The developed method removed feature spectra one by one and retrained the network, which increased computational cost, but the method led to a notable boost in performance. [PERSON] developed a hybrid feature band selection using the Grasshopper Optimization Algorithm (GOA) and MLP [30]. The authors found an optimal spectral set using GOA and evaluated the performance by classifying sonar data using the MLP network. Despite numerous feature spectra selection algorithms for HSI proposed in various research studies, none have consistently delivered optimal results across all applications. Moreover, many algorithms rely on a random search approach, which does not ensure the identification of the optimal feature spectra set. Classical feature selection algorithms evaluate the importance of individual features, which often fail to produce the optimal feature set for classification. Also, these methods often struggle to capture the high correlations and complex relationships inherent in spectral data, which can hinder optimal classification performance [31].