Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Hyperspectral imaging

  • 期刊论文

  • 发表日期：

    2026年2月26日

查看全文
  • Danfeng Hong,
  • Chenyu Li,
  • Naoto Yokoya,
  • Bing Zhang,
  • Xiuping Jia,
  • Antonio Plaza,
  • Paolo Gamba,
  • Jon Atli Benediktsson,
  • Jocelyn Chanussot

摘要

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.