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Multi-resolution terrestrial hyperspectral dataset for spectral unmixing problems.

Authors
  • Manohar Kumar, C V S S1
  • Jha, Sudhanshu Shekhar1
  • Nidamanuri, Rama Rao1
  • Dadhwal, Vinay Kumar2
  • 1 Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Valiamala, Thiruvananthapuram, Kerala, India. , (India)
  • 2 National Institute of Advanced Studies, Bengaluru, India. , (India)
Type
Published Article
Journal
Data in Brief
Publisher
Elsevier
Publication Date
Aug 01, 2022
Volume
43
Pages
108331–108331
Identifiers
DOI: 10.1016/j.dib.2022.108331
PMID: 35707244
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Recent developments in the miniaturization of hyperspectral imaging sensors have given rise to the increased use of hyperspectral imagery as the primary data for evaluating spectral unmixing algorithms in applications such as industrial quality control, agriculture, mineral mapping, military, etc. This article presents an ultra-high-resolution hyperspectral imagery dataset for undertaking benchmark studies on spectral unmixing. A terrestrial hyperspectral imager (THI) is used for imaging the target scene with the camera sensor pointing horizontally towards the target scene. The datasets are acquired at various spatial resolutions ranging from 1 mm to 2 cm. The targeted scene contains several paper-based panels, each size of 2 cm x 2 cm and filled with different colours and proportions, glued to a black background board that maintains a distinguishable distance between one another. In addition to the hyperspectral imagery data acquisitions, reference spectral signatures of the candidate mixture materials are obtained by in-situ hyperspectral reflectance measurements using a spectroradiometer. The hyperspectral image acquisition and the in-situ spectral signatures of the target scene are collected under natural illumination conditions. The proposed datasets are designed for undertaking proof-of-the-concept (PoC) studies in spectral unmixing. The datasets are also valuable for evaluating the performance of different statistical and machine learning algorithms for target detection, classification, and sub-pixel classification algorithms. © 2022 The Authors. Published by Elsevier Inc.

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