Collective Intelligence Using MFC-GAN With Independent Component Analysis Data Dimentionality Reduction for HIS Remote Sensing

This research paper introduces an innovative method to boost collective intelligence in hyperspectral image (HSI) remote sensing applications. Our approach combines a Multi-Fidelity Consistency Generative Adversarial Network (MFC-GAN) with Independent Component Analysis (ICA) to simplify the data. The MFC-GAN helps create high-quality HSI data that captures various spectral characteristics effectively. Then, we use ICA to further refine this generated data by reducing unnecessary information and noise. We tested our approach in experiments, and the results show its effectiveness in improving collective intelligence for HSI remote sensing tasks. It enhances classification accuracy and the way features are represented. In simpler terms, we developed a method to make hyperspectral image analysis better. We use a special computer program (MFC-GAN) to create high-quality image data and then clean it up with another technique (ICA). When we tested it, our method improved how well we can classify things in hyperspectral images and made the features we look at in these images clearer and more helpful.