Evaluating column subset selection methods for endmember extraction in hyperspectral unmixing

2020 ◽  
Author(s):  
Maher Aldeghlawi ◽  
Mohammed Q. Alkhatib ◽  
Miguel Velez-Reyes
Keyword(s):  
Subset Selection ◽  
Selection Methods ◽  
Endmember Extraction ◽  
Hyperspectral Unmixing ◽  
Column Subset Selection

scholarly journals DLR HySU—A Benchmark Dataset for Spectral Unmixing

2021 ◽  
Vol 13 (13) ◽  
pp. 2559
Author(s):  
Daniele Cerra ◽  
Miguel Pato ◽  
Kevin Alonso ◽  
Claas Köhler ◽  
Mathias Schneider ◽  
...  
Keyword(s):  
Spectral Unmixing ◽  
Benchmark Dataset ◽  
Data Sets ◽  
Endmember Extraction ◽  
Imaging Spectrometer ◽  
Hyperspectral Unmixing ◽  
Processing Step ◽  
German Aerospace ◽  
Spectrometer Data ◽  
First Time

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.

scholarly journals Electrode subset selection methods for an EEG-based P300 brain-computer interface

2014 ◽  
Vol 10 (3) ◽  
pp. 216-220 ◽  
Author(s):  
Michael T. McCann ◽  
David E. Thompson ◽  
Zeeshan H. Syed ◽  
Jane E. Huggins
Keyword(s):  
Brain Computer Interface ◽  
Subset Selection ◽  
Computer Interface ◽  
Selection Methods

scholarly journals Unsupervised Band Selection Method Based on Importance-Assisted Column Subset Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 517-527 ◽  
Author(s):  
Xiaoyan Luo ◽  
Zhiqi Shen ◽  
Rui Xue ◽  
Han Wan
Keyword(s):  
Subset Selection ◽  
Selection Method ◽  
Band Selection ◽  
Column Subset Selection

Bayesian Subset Selection Methods for Finding Engineering Design Values: an Application to Lumber Strength

Sankhya A ◽  
2018 ◽  
Vol 80 (S1) ◽  
pp. 146-172
Author(s):  
Yumi Kondo ◽  
James V Zidek ◽  
Carolyn G Taylor ◽  
Constance van Eeden
Keyword(s):  
Engineering Design ◽  
Subset Selection ◽  
Selection Methods ◽  
Design Values
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