Hyperspectral Imaging

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.

Figures (4)

• Figure 1: Overview of hyperspectral imaging. (A) Graphical illustration of the electromagnetic spectrum. (B) Expanded view of the typical wavelength regions captured in HSI: visible light (400-750 nm), near-infrared (NIR, 750-1400 nm), and shortwave infrared (SWIR, 1400-2500 nm). (C) Example hyperspectral scene illustrating integrated spatial and spectral information. (D) Qualitative spectral comparison of imaging modalities: panchromatic (single broadband), RGB (three broad bands), multispectral ($<$ 20 discrete bands), and hyperspectral ($>$ 100 contiguous narrow bands).
• Figure 2: Pipeline of HSI systems, from the to-be-captured scene to processed reflectance data. (A) Schematic of an imaging instrument to illustrate its core components. (B) Workflow of data acquisition, calibration, and processing to clarify the conversion from raw digital number (DN) values to radiance and final reflectance data. (C)-(F) Evolution of scanning techniques: (C) whiskbroom (point) scanning, (D) pushbroom (line) scanning, (E) staring (spectral) scanning--each employing multiple exposures, and (F) snapshot imaging, acquiring all spectral bands simultaneously in a single exposure without scanning.
• Figure 3: Overview of hyperspectral data analysis, from observed radiance or reflectance HSI data to low-level processing to high-level interpretation. (A) A general workflow of hyperspectral data analysis. (B) Schematic illustration of dimensionality reduction for HSI. (C) Visual comparison of original, PCA-reduced, and MNF-transformed features across three classifiers (KNN, SVM, RF), with magnified views highlighting differences, and a trend plot showing classification accuracy versus the number of spectral bands. (E) Conceptual diagram of spectral unmixing for HSI. (F) Geometric interpretation of the pure pixel assumption, using toy datasets with and without pure pixels. (G) Workflow of endmember acquisition from HSI data. (H) Visualization of unmixing results, including identified or extracted endmembers and corresponding abundance maps for different materials.
• Figure 4: Illustrative examples of HSI applications. Representative use cases of HSI in environmental observation, agriculture and forestry, biomedicine, industrial and food inspection, cultural heritage and forensics, as well as security and defense, are depicted.