AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution
David S. Bhatti, Yougin Choi, Rahman S M Wahidur, Maleeka Bakhtawar, Sumin Kim, Surin Lee, Yongtae Lee, Heung-No Lee
TL;DR
An overview of the HSI, its applications, challenges in data fusion and the role of deep learning models in processing HSI data is provided, and how integration of multimodal HSI with AI, particularly with deep learning, improves classification accuracy and operational efficiency is discussed.
Abstract
Hyperspectral imaging (HSI) captures spatial and spectral data, enabling analysis of features invisible to conventional systems. The technology is vital in fields such as weather monitoring, food quality control, counterfeit detection, healthcare diagnostics, and extending into defense, agriculture, and industrial automation at the same time. HSI has advanced with improvements in spectral resolution, miniaturization, and computational methods. This study provides an overview of the HSI, its applications, challenges in data fusion and the role of deep learning models in processing HSI data. We discuss how integration of multimodal HSI with AI, particularly with deep learning, improves classification accuracy and operational efficiency. Deep learning enhances HSI analysis in areas like feature extraction, change detection, denoising unmixing, dimensionality reduction, landcover mapping, data augmentation, spectral construction and super resolution. An emerging focus is the fusion of hyperspectral cameras with large language models (LLMs), referred as highbrain LLMs, enabling the development of advanced applications such as low visibility crash detection and face antispoofing. We also highlight key players in HSI industry, its compound annual growth rate and the growing industrial significance. The purpose is to offer insight to both technical and non-technical audience, covering HSI's images, trends, and future directions, while providing valuable information on HSI datasets and software libraries.