Enhancing Hyperspectral Image Classification with Bayesian for CNN-GRU Hyperparameter Optimization

Hyperspectral image classification, which utilizes algorithms to analyze a vast spectrum of wavelengths beyond the visible, is pivotal for tasks like environmental monitoring, precision agriculture, and resource management. This field has seen significant enhancements with the advent of advanced machine learning techniques and increased computational capabilities. In our study, we focus on the hyperparameter optimization of an existing framework that combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). This optimization, particularly through Bayesian optimization, fine-tunes the model to better harness CNNs’ spatial feature extraction and GRUs’ sequential data analysis capabilities, crucial for understanding complex spatial and temporal dynamics. Our approach demonstrates considerable improvements in accuracy and computational efficiency across various datasets by optimizing the neural network architecture. The employment of Bayesian optimization for hyperparameter tuning has shown to significantly enhance model performance, evidenced by a substantial 3% increase in classification accuracy (from 92% to 95%) compared to previous methodologies reliant on trial and error. This advancement underscores the importance of precise hyperparameter tuning in deep learning applications for hyperspectral image classification, reaffirming the potential of existing models when optimally configured.