mlsanityData to 14 Oct 2026

AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution

arXiv 2502.06894 ↗First posted 9 Feb 2025cs.CV cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 53 of its 504 references that are in the map. First posted on arXiv 9 Feb 2025.

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53 of 504 references.

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38 more
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Citing papers per year: 2025: 1.

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  1. 2025HyperCap: Hyperspectral Land Cover Captioning Dataset for Vision Language Modelscited by 3