Identification of Cotton Using Random Forest Based on Wide-Band Imaging Spectrometer Data of Tiangong-2

2018 ◽  
pp. 264-276
Author(s):  
Xiaojun She ◽  
Kangyu Fu ◽  
Jie Wang ◽  
Wenchao Qi ◽  
Xiaolu Li ◽  
...  
Keyword(s):  
Random Forest ◽  
Wide Band ◽  
Imaging Spectrometer ◽  
Spectrometer Data

scholarly journals DATA QUALITY EVALUATION AND APPLICATION POTENTIAL ANALYSIS OF TIANGONG-2 WIDE-BAND IMAGING SPECTROMETER

2018 ◽  
Vol XLII-3 ◽  
pp. 1427-1431 ◽  
Author(s):  
B. Qin ◽  
L. Li ◽  
S. Li
Keyword(s):  
Data Quality ◽  
Quality Evaluation ◽  
Remote Sensing Data ◽  
Wide Band ◽  
Landsat 8 ◽  
Image Sharpness ◽  
Radiation Quality ◽  
Imaging Spectrometer ◽  
Spectrometer Data

Tiangong-2 is the first space laboratory in China, which launched in September 15, 2016. Wide-band Imaging Spectrometer is a medium resolution multispectral imager on Tiangong-2. In this paper, the authors introduced the indexes and parameters of Wideband Imaging Spectrometer, and made an objective evaluation about the data quality of Wide-band Imaging Spectrometer in radiation quality, image sharpness and information content, and compared the data quality evaluation results with that of Landsat-8. Although the data quality of Wide-band Imager Spectrometer has a certain disparity with Landsat-8 OLI data in terms of signal to noise ratio, clarity and entropy. Compared with OLI, Wide-band Imager Spectrometer has more bands, narrower bandwidth and wider swath, which make it a useful remote sensing data source in classification and identification of large and medium scale ground objects. In the future, Wide-band Imaging Spectrometer data will be widely applied in land cover classification, ecological environment assessment, marine and coastal zone monitoring, crop identification and classification, and other related areas.

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.

Research of aerial imaging spectrometer data acquisition technology based on USB 3.0

2016 ◽  
Author(s):  
Junze Huang ◽  
Yueming Wang ◽  
Daogang He ◽  
Yanan Yu
Keyword(s):  
Data Acquisition ◽  
Imaging Spectrometer ◽  
Aerial Imaging ◽  
Spectrometer Data

Estimation of Field Scale Topsoil Properties of Agronomic Interest from Prisma Imaging Spectrometer Data

2021 ◽  
Author(s):  
R. Casa ◽  
M. Tolomio ◽  
N. Mzid ◽  
S. Pignatti ◽  
S. Pascucci
Keyword(s):  
Field Scale ◽  
Imaging Spectrometer ◽  
Spectrometer Data

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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