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Multi-resolution terrestrial hyperspectral dataset for spectral unmixing problems.

C V S S Manohar Kumar1, Sudhanshu Shekhar Jha1, Rama Rao Nidamanuri1

  • 1Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Valiamala, Thiruvananthapuram, Kerala, India.

Data in Brief
|June 16, 2022
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Summary
This summary is machine-generated.

This study introduces an ultra-high-resolution hyperspectral imagery dataset for spectral unmixing algorithm evaluation. The dataset supports benchmark studies in diverse applications like agriculture and mineral mapping.

Keywords:
BFM, background-free-mixtureHyperspectral remote sensingMBM, multiple-background-mixtureMixture modellingPoC, proof-of-the-conceptSBM, single-background-mixtureSpectral unmixingSpectroradiometerTHI, terrestrial hyperspectral imagerTarget detection

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Data Science

Background:

  • Miniaturization of hyperspectral imaging sensors enables new applications.
  • Hyperspectral imagery is crucial for evaluating spectral unmixing algorithms.
  • Existing datasets may not meet the demands of ultra-high-resolution analysis.

Purpose of the Study:

  • To present a novel ultra-high-resolution hyperspectral imagery dataset.
  • To facilitate benchmark studies for spectral unmixing algorithms.
  • To support research in target detection, classification, and sub-pixel classification.

Main Methods:

  • Acquisition of hyperspectral imagery using a terrestrial hyperspectral imager (THI).
  • Data collection at spatial resolutions from 1 mm to 2 cm.
  • In-situ spectral signature measurements using a spectroradiometer under natural illumination.

Main Results:

  • A comprehensive dataset featuring paper-based panels with varying colors and proportions on a black background.
  • Reference spectral signatures of mixture materials obtained.
  • Data acquired under natural lighting conditions for realistic scenarios.

Conclusions:

  • The proposed dataset is ideal for proof-of-concept (PoC) studies in spectral unmixing.
  • Valuable resource for assessing statistical and machine learning algorithms.
  • Enables advanced analysis in industrial quality control, agriculture, mineral mapping, and military applications.