A dataset with ground-truth for hyperspectral unmixing

Min Zhao, Jie Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Spectral unmixing is one of the most important issues of hyperspectral data processing. However, the lack of publicly available dataset with ground-truth makes it difficult to evaluate and compare the performance of unmixing algorithms. In this work, we create several experimental scenes in our laboratory with controlled settings where the pure material spectra and material compositions are known. Lab-made hyperspectral datasets with these scenes are then provided, and mutually validated with typical linear and nonlinear unmixing algorithms.

Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5077-5080
Number of pages4
ISBN (Electronic)9781538671504
DOIs
StatePublished - 31 Oct 2018
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2018-July

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/TerritorySpain
CityValencia
Period22/07/1827/07/18

Keywords

  • Ground-truth
  • Hyperspectral imaging
  • Spectral unmixing
  • Unmixing database

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