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Hyperspectral imaging
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DOI：10.1038/s43586-026-00470-x.png)
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Abstract
En 中文
Hyperspectral imaging (HSI) is an advanced sensing modality that captures spatial and spectral information simultaneously, enabling non-invasive and label-free characterization of material, chemical and biological properties. This Primer overviews HSI, with an emphasis on the imaging workflow and data processing pipeline. We introduce the essential physical principles and sensor architectures, using Earth observation HSI systems as a representative example. Key steps in data acquisition, calibration and correction that determine data structure and quality are discussed. We summarize common hyperspectral data forms and highlight core analytical techniques, including dimensionality reduction, classification and spectral unmixing, together with emerging artificial intelligence-based methods that increasingly influence current HSI research. Six representative application fields are briefly surveyed to illustrate how HSI enables sub-visual feature extraction for quantitative interpretation and decision-making. We also discuss persistent challenges, including hardware trade-offs, acquisition variability and high-dimensional complexity alongside promising advances such as computational imaging, physics-guided modelling, cross-modal fusion and scalable learning frameworks. Best practices for dataset sharing, reproducibility and metadata documentation to support transparent research are included. Scalable, real-time and embedded HSI, enabled by sensor miniaturization and artificial intelligence, will push HSI into a cross-disciplinary platform for transformative scientific and societal impact.
Keywords:
SPATIAL INFORMATION
MULTISPECTRAL DATA
SPECTROMETER
SPECTROSCOPY
ALGORITHM
NETWORK
QUALITY
Journal
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56
Papers:
102
Citations:
9.3K
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