Effective Machine Learning-Based Strategies for Enhancing the Functionality of Hyper Spectral Imaging

Abstract

Hyperspectral Imaging (HSI) is a powerful technology for capturing high-dimensional spectral information across a wide range of applications, including remote sensing, medical diagnostics, and material analysis. However, extracting meaningful insights from HSI data presents unique challenges due to its complex nature and high dimensionality. This study explores innovative machine learning-based strategies aimed at enhancing the functionality of HSI in various applications. One key approach investigated in this research is the implementation of Deep Learning-based frameworks, with a particular focus on Generative Adversarial Networks (GANs). GANs, equipped with Convolutional Neural Networks (CNNs) as generators, which are utilized to create data points that are not only statistically accurate but also semantically meaningful in relation to the underlying distribution. Through an adversarial objective function, the generator is trained to predict the distribution, while the discriminator network distinguishes between real and generated data. This adversarial game between the discriminator and generator has shown promise in improving HSI classification efficiency. Furthermore, the proposed GAN framework incorporates a classifier component capable of categorizing HSI samples based on their spectral characteristics, using a novel algorithm called the Generative Model-based Hybrid Approach (GMHA-HSIC). This approach adds a valuable dimension to HSI data analysis by enabling accurate classification based on spectral features.