Reinforcement Learning based Hyperspectral Image Classification using Binary Entropy Method

This paper presents an approach for hyperspectral image classification using reinforcement learning techniques, specifically employing the binary entropy method. Hyperspectral imaging has garnered significant interest due to its ability to capture rich spectral information, yet its high dimensionality poses challenges for classification. The proposed method leverages reinforcement learning (RL) to intelligently select informative spectral bands, optimizing classification accuracy. By utilizing the binary entropy method to guide the selection of spectral bands, the RL- based approach simultaneously addresses feature selection and classification. Experiments conducted on benchmark hyperspectral datasets demonstrate that the RL-driven binary entropy method outperforms traditional classifiers in terms of accuracy and robustness. This study underscores the potential of combining reinforcement learning with hyperspectral imaging to improve classification outcomes in remote sensing applications. Keywords: Hyperspectral image classification, reinforcement learning, binary entropy method, feature selection, remote sensing, spectral analysis, high- dimensional data.