DLR HySU—A Benchmark Dataset for Spectral Unmixing
Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmix...
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doaj-de27b5cb690844d2af4f8390a5e80cde2021-07-15T15:44:29ZengMDPI AGRemote Sensing2072-42922021-06-01132559255910.3390/rs13132559DLR HySU—A Benchmark Dataset for Spectral UnmixingDaniele Cerra0Miguel Pato1Kevin Alonso2Claas Köhler3Mathias Schneider4Raquel de los Reyes5Emiliano Carmona6Rudolf Richter7Franz Kurz8Peter Reinartz9Rupert Müller10Remote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 Weßling, GermanyRemote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 Weßling, GermanyRemote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 Weßling, GermanyRemote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 Weßling, GermanyRemote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 Weßling, GermanyRemote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 Weßling, GermanyRemote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 Weßling, GermanyRemote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 Weßling, GermanyRemote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 Weßling, GermanyRemote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 Weßling, GermanyRemote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 Weßling, GermanySpectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper, we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixel assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.https://www.mdpi.com/2072-4292/13/13/2559spectral unmixingimaging spectrometerhyperspectralbenchmark datasetdimensionality estimationendmember extraction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Daniele Cerra Miguel Pato Kevin Alonso Claas Köhler Mathias Schneider Raquel de los Reyes Emiliano Carmona Rudolf Richter Franz Kurz Peter Reinartz Rupert Müller |
spellingShingle |
Daniele Cerra Miguel Pato Kevin Alonso Claas Köhler Mathias Schneider Raquel de los Reyes Emiliano Carmona Rudolf Richter Franz Kurz Peter Reinartz Rupert Müller DLR HySU—A Benchmark Dataset for Spectral Unmixing Remote Sensing spectral unmixing imaging spectrometer hyperspectral benchmark dataset dimensionality estimation endmember extraction |
author_facet |
Daniele Cerra Miguel Pato Kevin Alonso Claas Köhler Mathias Schneider Raquel de los Reyes Emiliano Carmona Rudolf Richter Franz Kurz Peter Reinartz Rupert Müller |
author_sort |
Daniele Cerra |
title |
DLR HySU—A Benchmark Dataset for Spectral Unmixing |
title_short |
DLR HySU—A Benchmark Dataset for Spectral Unmixing |
title_full |
DLR HySU—A Benchmark Dataset for Spectral Unmixing |
title_fullStr |
DLR HySU—A Benchmark Dataset for Spectral Unmixing |
title_full_unstemmed |
DLR HySU—A Benchmark Dataset for Spectral Unmixing |
title_sort |
dlr hysu—a benchmark dataset for spectral unmixing |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-06-01 |
description |
Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper, we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixel assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications. |
topic |
spectral unmixing imaging spectrometer hyperspectral benchmark dataset dimensionality estimation endmember extraction |
url |
https://www.mdpi.com/2072-4292/13/13/2559 |
work_keys_str_mv |
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