INTRODUCING DLR HYSU - A BENCHMARK DATASET FOR SPECTRAL UNMIXING

被引:0
作者
Cerra, Daniele [1 ]
Pato, Miguel [1 ]
Alonso, Kevin [1 ]
Koehler, Claas [1 ]
Schneider, Mathias [1 ]
Reyes, Raquel de los [1 ]
Carmona, Emiliano [1 ]
Richter, Rudolf [1 ]
Kurz, Franz [1 ]
Mueller, Rupert [1 ]
Reinartz, Peter [1 ]
机构
[1] German Aerosp Ctr DLR, Earth Observat Ctr EOC, Remote Sensing Technol Inst MF, Munchnerstr 20, D-82234 Wessling, Germany
来源
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年
关键词
Spectral unmixing; benchmark dataset; dimensionality reduction; endmember extraction; abundance estimation; HySpex;
D O I
10.1109/IGARSS52108.2023.10281975
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The DLR HyperSpectral Unmixing (DLR HySU) open benchmark dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes on a homogeneous background, complemented by simultaneous ground-based reflectance measurements. The dataset allows assessing dimensionality estimation, endmember extraction with and without pure pixel assumption, and abundance estimation in the frame of spectral unmixing applications, enabling estimations at sub-pixel level. This paper presents the first works in the literature using the dataset, which demonstrate that DLR HySU is filling a gap regarding validation using real imaging spectrometer data with accurately measured targets.
引用
收藏
页码:1008 / 1011
页数:4
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