scholarly journals DATA PROCESSING FOR THE SPACE-BASED DESIS HYPERSPECTRAL SENSOR

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 271-277 ◽  
Author(s):  
E. Carmona ◽  
J. Avbelj ◽  
K. Alonso ◽  
M. Bachmann ◽  
D. Cerra ◽  
...  
Keyword(s):  
Data Processing ◽  
Spectral Resolution ◽  
Space Station ◽  
Hyperspectral Data ◽  
High Spectral Resolution ◽  
Imaging Spectrometer ◽  
Cmos Sensor ◽  
Global Coverage ◽  
German Aerospace ◽  
Hyperspectral Sensor

The German Aerospace Center (DLR) and Teledyne Brown Engineering (TBE) have established a collaboration to develop and operate a new space-based hyperspectral sensor, the DLR Earth Sensing Imaging Spectrometer (DESIS). DESIS will provide spacebased hyperspectral data in the VNIR with high spectral resolution and near-global coverage. While TBE provides the platform and infrastructure for operation of the DESIS instrument on the International Space Station, DLR is responsible for providing the instrument and the processing software. The DESIS instrument is equipped with novel characteristics for an imaging spectrometer such high spectral resolution (2.55 nm), a mirror pointing unit or a CMOS sensor operated in rolling shutter mode. We present here an overview of the DESIS instrument and its processing chain, emphasizing the effect of the novel characteristics of DESIS in the data processing and final data products. Furthermore, we analyse in more detail the effect of the rolling shutter on the DESIS data and possible mitigation/correction strategies.

scholarly journals The Instrument Design of the DLR Earth Sensing Imaging Spectrometer (DESIS)

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1622 ◽  
Author(s):  
David Krutz ◽  
Rupert Müller ◽  
Uwe Knodt ◽  
Burghardt Günther ◽  
Ingo Walter ◽  
...  
Keyword(s):  
International Space Station ◽  
Near Infrared ◽  
Space Station ◽  
Hyperspectral Data ◽  
Imaging Spectrometer ◽  
International Space ◽  
Spectral Bands ◽  
Sampling Distance ◽  
Characterization Of Materials ◽  
German Aerospace

Whether for identification and characterization of materials or for monitoring of theenvironment, space-based hyperspectral instruments are very useful. Hyperspectral instrumentsmeasure several dozens up to hundreds of spectral bands. These data help to reconstruct the spectralproperties like reflectance or emission of Earth surface or the absorption of the atmosphere, and toidentify constituents on land, water, and in the atmosphere. There are a lot of possible applications,from vegetation and water quality up to greenhouse gas monitoring. But the actual number ofhyperspectral space-based missions or hyperspectral space-based data is limited. This will be changedin the next years by different missions. The German Aerospace Center (DLR) Earth Sensing ImagingSpectrometer (DESIS) is one of the new currently existing space-based hyperspectral instruments,launched in 2018 and ready to reduce the gap of space-born hyperspectral data. The instrument isoperating onboard the International Space Station, using the Multi-User System for Earth Sensing(MUSES) platform. The instrument has 235 spectral bands in the wavelength range from visible(400 nm) to near-infrared (1000 nm), which results in a 2.5 nm spectral sampling distance and aground sampling distance of 30 m from 400 km orbit of the International Space Station. In this article,the design of the instrument will be described.

scholarly journals THE NEW HYPERSPECTRAL SENSOR DESIS ON THE MULTI-PAYLOAD PLATFORM MUSES INSTALLED ON THE ISS

2016 ◽  
Vol XLI-B1 ◽  
pp. 461-467 ◽  
Author(s):  
R. Müller ◽  
J. Avbelj ◽  
E. Carmona ◽  
A. Eckardt ◽  
B. Gerasch ◽  
...  
Keyword(s):  
Space Station ◽  
Image Data ◽  
Imaging Spectrometer ◽  
Ground Segment ◽  
Data Policy ◽  
Operational Phase ◽  
German Aerospace ◽  
Space Segment ◽  
Hyperspectral Sensor ◽  
Data Calibration

The new hyperspectral instrument DLR Earth Sensing Imaging Spectrometer (DESIS) will be developed and integrated in the Multi-User-System for Earth Sensing (MUSES) platform installed on the International Space Station (ISS). The DESIS instrument will be launched to the ISS mid of 2017 and robotically installed in one of the four slots of the MUSES platform. After a four month commissioning phase the operational phase will last at least until 2020. The MUSES / DESIS system will be commanded and operated by the publically traded company TBE (Teledyne Brown Engineering), which initiated the whole program. TBE provides the MUSES platform and the German Aerospace Center (DLR) develops the instrument DESIS and establishes a Ground Segment for processing, archiving, delivering and calibration of the image data mainly used for scientific and humanitarian applications. Well calibrated and harmonized products will be generated together with the Ground Segment established at Teledyne. The article describes the Space Segment consisting of the MUSES platform and the instrument DESIS as well as the activities at the two (synchronized) Ground Segments consisting of the processing methods, product generation, data calibration and product validation. Finally comments to the data policy are given.

scholarly journals THE NEW HYPERSPECTRAL SENSOR DESIS ON THE MULTI-PAYLOAD PLATFORM MUSES INSTALLED ON THE ISS

2016 ◽  
Vol XLI-B1 ◽  
pp. 461-467
Author(s):  
R. Müller ◽  
J. Avbelj ◽  
E. Carmona ◽  
A. Eckardt ◽  
B. Gerasch ◽  
...  
Keyword(s):  
Space Station ◽  
Image Data ◽  
Imaging Spectrometer ◽  
Ground Segment ◽  
Data Policy ◽  
Operational Phase ◽  
German Aerospace ◽  
Space Segment ◽  
Hyperspectral Sensor ◽  
Data Calibration

The new hyperspectral instrument DLR Earth Sensing Imaging Spectrometer (DESIS) will be developed and integrated in the Multi-User-System for Earth Sensing (MUSES) platform installed on the International Space Station (ISS). The DESIS instrument will be launched to the ISS mid of 2017 and robotically installed in one of the four slots of the MUSES platform. After a four month commissioning phase the operational phase will last at least until 2020. The MUSES / DESIS system will be commanded and operated by the publically traded company TBE (Teledyne Brown Engineering), which initiated the whole program. TBE provides the MUSES platform and the German Aerospace Center (DLR) develops the instrument DESIS and establishes a Ground Segment for processing, archiving, delivering and calibration of the image data mainly used for scientific and humanitarian applications. Well calibrated and harmonized products will be generated together with the Ground Segment established at Teledyne. The article describes the Space Segment consisting of the MUSES platform and the instrument DESIS as well as the activities at the two (synchronized) Ground Segments consisting of the processing methods, product generation, data calibration and product validation. Finally comments to the data policy are given.

scholarly journals Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

2021 ◽  
Vol 13 (9) ◽  
pp. 1693
Author(s):  
Anushree Badola ◽  
Santosh K. Panda ◽  
Dar A. Roberts ◽  
Christine F. Waigl ◽  
Uma S. Bhatt ◽  
...  
Keyword(s):  
Land Cover ◽  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Fuel Mapping ◽  
Sentinel 2

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

Analysis and Optical Design of Very High Spectral Resolution Imaging Spectrometer of the Atmospheric CO2

2015 ◽  
Vol 35 (7) ◽  
pp. 0722001
Author(s):  
宋文宝 Song Wenbao ◽  
靳阳明 Jin Yangming ◽  
赵知诚 Zhao Zhicheng ◽  
沈为民 Shen Weimin ◽  
范东栋 Fan Dongdong
Keyword(s):  
Spectral Resolution ◽  
Optical Design ◽  
Atmospheric Co2 ◽  
High Spectral Resolution ◽  
Imaging Spectrometer ◽  
Resolution Imaging ◽  
Very High

scholarly journals Target detection algorithm for airborne thermal hyperspectral data

2014 ◽  
Vol XL-8 ◽  
pp. 827-832 ◽  
Author(s):  
R. Marwaha ◽  
A. Kumar ◽  
P. L. N. Raju ◽  
Y. V. N. Krishna Murthy
Keyword(s):  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Hyperspectral Data ◽  
High Spectral Resolution ◽  
Infrared Range ◽  
Digital Photograph ◽  
Minimum Noise Fraction ◽  
Noise Fraction ◽  
Minimum Noise

Airborne hyperspectral imaging is constantly being used for classification purpose. But airborne thermal hyperspectral image usually is a challenge for conventional classification approaches. The Telops Hyper-Cam sensor is an interferometer-based imaging system that helps in the spatial and spectral analysis of targets utilizing a single sensor. It is based on the technology of Fourier-transform which yields high spectral resolution and enables high accuracy radiometric calibration. The Hypercam instrument has 84 spectral bands in the 868 cm<sup>&minus;1</sup> to 1280 cm<sup>&minus;1</sup> region (7.8 μm to 11.5 μm), at a spectral resolution of 6 cm<sup>&minus;1</sup> (full-width-half-maximum) for LWIR (long wave infrared) range. Due to the Hughes effect, only a few classifiers are able to handle high dimensional classification task. MNF (Minimum Noise Fraction) rotation is a data dimensionality reducing approach to segregate noise in the data. In this, the component selection of minimum noise fraction (MNF) rotation transformation was analyzed in terms of classification accuracy using constrained energy minimization (CEM) algorithm as a classifier for Airborne thermal hyperspectral image and for the combination of airborne LWIR hyperspectral image and color digital photograph. On comparing the accuracy of all the classified images for airborne LWIR hyperspectral image and combination of Airborne LWIR hyperspectral image with colored digital photograph, it was found that accuracy was highest for MNF component equal to twenty. The accuracy increased by using the combination of airborne LWIR hyperspectral image with colored digital photograph instead of using LWIR data alone.

scholarly journals Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data

2020 ◽  
Vol 13 (1) ◽  
pp. 107
Author(s):  
Anna Jarocińska ◽  
Dominik Kopeć ◽  
Barbara Tokarska-Guzik ◽  
Edwin Raczko
Keyword(s):  
Spectral Resolution ◽  
Laser Scanning ◽  
Biodiversity Loss ◽  
Functional Trait ◽  
Early Summer ◽  
Hyperspectral Data ◽  
High Spectral Resolution ◽  
Optical Data ◽  
Lidar Data ◽  
Spectral Ranges

The study was focused on a plant native to Poland, the European dewberry Rubus caesius L., which is a species with the ability to become excessively abundant within its original range, potentially causing significant changes in ecosystems, including biodiversity loss. Monitoring plant distributions over large areas requires mapping that is fast, reliable, and repeatable. For Rubus, different types of data were successfully used for classification, but most of the studies used data with a very high spectral resolution. The aim of this study was to indicate, using hyperspectral and Light Detection and Ranging (LiDAR) data, the main functional trait crucial for R. caesius differentiation from non-Rubus. This analysis was carried out with consideration of the seasonal variability and different percentages of R. caesius in the vegetation patches. The analysis was based on hyperspectral HySpex images and Airborne Laser Scanning (ALS) products. Data were acquired during three campaigns: early summer, summer, and autumn. Differentiation based on Linear Discriminate Analysis (LDA) and Non-Parametric Multivariate Analysis of Variance (NPMANOVA) analysis was successful for each of the analysed campaigns using optical data, but the ALS data were less useful for identification. The analysis indicated that selected spectral ranges (VIS, red-edge, and parts of the NIR and possibly SWIR ranges) can be useful for differentiating R. caesius from non-Rubus. The most useful indices were ARI1, CRI1, ARVI, GDVI, CAI, NDNI, and MRESR. The obtained results indicate that it is possible to classify R. caesius using images with lower spectral resolution than hyperspectral data.

VIRTIS-H: a high-spectral-resolution channel for the Rosetta infrared imaging spectrometer

2000 ◽  
Author(s):  
Pierre Drossart ◽  
Alain Semery ◽  
Marc Bouye ◽  
Yann Hello ◽  
Gerard Huntzinger ◽  
...  
Keyword(s):  
Spectral Resolution ◽  
Infrared Imaging ◽  
High Spectral Resolution ◽  
Imaging Spectrometer
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