PC software performance research of aerial imaging spectrometer data acquisition based on USB3.0

2017 ◽  
Author(s):  
Junze Huang ◽  
Yueming Wang ◽  
Daogang He ◽  
Yanan Yu
Keyword(s):  
Data Acquisition ◽  
Software Performance ◽  
Imaging Spectrometer ◽  
Aerial Imaging ◽  
Spectrometer Data ◽  
Performance Research

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.

Leveraging Time Series Imaging Spectrometer Data and Deep Learning for Methane Plume Detection and Delineation

2022 ◽  
Author(s):  
Patrick Sullivan ◽  
Kelly O'Neill ◽  
Andrew Thorpe ◽  
Riley Duren ◽  
Philip Dennison
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Imaging Spectrometer ◽  
Spectrometer Data

Minimizing soil moisture variations in multi-temporal airborne imaging spectrometer data for digital soil mapping

Geoderma ◽  
2019 ◽  
Vol 337 ◽  
pp. 607-621 ◽  
Author(s):  
Sanne Diek ◽  
Sabine Chabrillat ◽  
Marco Nocita ◽  
Michael E. Schaepman ◽  
Rogier de Jong
Keyword(s):  
Soil Moisture ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Imaging Spectrometer ◽  
Multi Temporal ◽  
Airborne Imaging ◽  
Spectrometer Data
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