Hyperspectral Imaging

• URL: http://arxiv.org/abs/2508.08107v1
• Date: Mon, 11 Aug 2025 15:47:24 GMT
• Title: Hyperspectral Imaging
• Authors: Danfeng Hong, Chenyu Li, Naoto Yokoya, Bing Zhang, Xiuping Jia, Antonio Plaza, Paolo Gamba, Jon Atli Benediktsson, Jocelyn Chanussot,
• Abstract summary: Hyperspectral imaging (HSI) is an advanced sensing modality that simultaneously captures spatial and spectral information.<n>This Primer presents a comprehensive overview of HSI, from the underlying physical principles and sensor architectures to key steps in data acquisition, calibration, and correction.
• Score: 49.45523645429475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral imaging (HSI) is an advanced sensing modality that simultaneously captures spatial and spectral information, enabling non-invasive, label-free analysis of material, chemical, and biological properties. This Primer presents a comprehensive overview of HSI, from the underlying physical principles and sensor architectures to key steps in data acquisition, calibration, and correction. We summarize common data structures and highlight classical and modern analysis methods, including dimensionality reduction, classification, spectral unmixing, and AI-driven techniques such as deep learning. Representative applications across Earth observation, precision agriculture, biomedicine, industrial inspection, cultural heritage, and security are also discussed, emphasizing HSI's ability to uncover sub-visual features for advanced monitoring, diagnostics, and decision-making. Persistent challenges, such as hardware trade-offs, acquisition variability, and the complexity of high-dimensional data, are examined alongside emerging solutions, including computational imaging, physics-informed modeling, cross-modal fusion, and self-supervised learning. Best practices for dataset sharing, reproducibility, and metadata documentation are further highlighted to support transparency and reuse. Looking ahead, we explore future directions toward scalable, real-time, and embedded HSI systems, driven by sensor miniaturization, self-supervised learning, and foundation models. As HSI evolves into a general-purpose, cross-disciplinary platform, it holds promise for transformative applications in science, technology, and society.

Related papers

• Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration [63.61423859450929]
  This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses.<n>We identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery.
  arXiv  Detail & Related papers  (2026-01-20T18:46:42Z)
• From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM [52.64097278841485]
  Review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions.<n>Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques.
  arXiv  Detail & Related papers  (2025-09-25T14:15:43Z)
• Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation [14.306027161664565]
  Generative artificial intelligence (AI) is rapidly transforming medical imaging.<n>Generative AI contributes to key stages of the imaging continuum from acquisition and reconstruction to cross-modality synthesis.<n>This review aims to guide future research and foster interdisciplinary collaboration at the intersection of AI, medicine, and biomedical engineering.
  arXiv  Detail & Related papers  (2025-08-07T07:58:40Z)
• Benchmarking and Evaluation of AI Models in Biology: Outcomes and Recommendations from the CZI Virtual Cells Workshop [18.00029758641004]
  We aim to accelerate the development of robust benchmarks for AI driven Virtual Cells.<n>These benchmarks are crucial for ensuring rigor, relevance, and biological relevance.<n>These benchmarks will advance the field toward integrated models that drive new discoveries, therapeutic insights, and a deeper understanding of cellular systems.
  arXiv  Detail & Related papers  (2025-07-14T17:25:28Z)
• CRIA: A Cross-View Interaction and Instance-Adapted Pre-training Framework for Generalizable EEG Representations [52.251569042852815]
  CRIA is an adaptive framework that utilizes variable-length and variable-channel coding to achieve a unified representation of EEG data across different datasets.<n>The model employs a cross-attention mechanism to fuse temporal, spectral, and spatial features effectively.<n> Experimental results on the Temple University EEG corpus and the CHB-MIT dataset show that CRIA outperforms existing methods with the same pre-training conditions.
  arXiv  Detail & Related papers  (2025-06-19T06:31:08Z)
• Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
  Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
  arXiv  Detail & Related papers  (2025-06-11T03:29:18Z)
• SITE: towards Spatial Intelligence Thorough Evaluation [121.1493852562597]
  Spatial intelligence (SI) represents a cognitive ability encompassing the visualization, manipulation, and reasoning about spatial relationships.<n>We introduce SITE, a benchmark dataset towards SI Thorough Evaluation.<n>Our approach to curating the benchmark combines a bottom-up survey about 31 existing datasets and a top-down strategy drawing upon three classification systems in cognitive science.
  arXiv  Detail & Related papers  (2025-05-08T17:45:44Z)
• Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions [19.926445428206776]
  Large Language Models (LLMs) are transforming scientific hypothesis generation and validation.<n>This survey provides a structured overview of LLM-driven approaches.<n>We examine techniques such as retrieval-augmented generation, knowledge-graph completion, simulation, causal inference, and tool-assisted reasoning.
  arXiv  Detail & Related papers  (2025-05-06T19:22:23Z)
• Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications [0.15705429611931052]
  Small object detection (SOD) is a critical yet challenging task in computer vision.<n>Recent advancements in deep learning have introduced innovative solutions.<n>Emerging trends such as lightweight neural networks, knowledge distillation (KD), and self-supervised learning offer promising directions for improving detection efficiency.
  arXiv  Detail & Related papers  (2025-03-26T12:58:13Z)
• AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution [1.2647816797166165]
  Hyperspectral imaging (HSI) captures spatial and spectral data, enabling analysis of features invisible to conventional systems.<n>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.<n>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.
  arXiv  Detail & Related papers  (2025-02-09T12:44:16Z)
• HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model [88.13261547704444]
  Hyper SIGMA is a vision transformer-based foundation model that unifies HSI interpretation across tasks and scenes.<n>In addition, we construct a large-scale hyperspectral dataset, HyperGlobal-450K, for pre-training, which contains about 450K hyperspectral images.
  arXiv  Detail & Related papers  (2024-06-17T13:22:58Z)
• Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing [57.52865154829273]
  Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience remote sensing (RS) In the past decade efforts have been made to process analyze these hyperspectral (HS) products mainly by means of seasoned experts. For this reason, it is urgent to develop more intelligent and automatic approaches for various HS RS applications.
  arXiv  Detail & Related papers  (2021-03-02T03:32:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.