A Laboratory-Created Dataset with Ground Truth for Hyperspectral Unmixing Evaluation

Min Zhao, Jie Chen, Zhe He

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Spectral unmixing is an important and challenging problem in hyperspectral data processing. This topic has been extensively studied and a variety of unmixing algorithms have been proposed in the literature. However, the lack of publicly available datasets with ground truth makes it difficult to evaluate and compare the performance of unmixing algorithms in a quantitative and objective manner. Most of the existing works rely on the use of numerical synthetic data and an intuitive inspection of the results of real data. To alleviate this dilemma, in this study, we design several experimental scenes in our laboratory, including printed checkerboards, mixed quartz sands, and reflection with a vertical board. A dataset is then created by imaging these scenes with the hyperspectral camera in our laboratory, providing 36 mixtures with more than 130 000 pixels with 256 wavelength bands ranging from 400 to 1000 nm. The experimental settings are strictly controlled so that pure material spectral signatures and material compositions are known. To the best of our knowledge, this dataset is the first publicly available dataset created in a systematic manner with ground truth for spectral unmixing. Some typical linear and nonlinear unmixing algorithms are also tested with this dataset and lead to meaningful results.

Original languageEnglish
Article number8681610
Pages (from-to)2170-2183
Number of pages14
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume12
Issue number7
DOIs
StatePublished - Jul 2019

Keywords

  • Ground truth
  • hyperspectral imaging
  • spectral unmixing
  • unmixing database

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