The New Hyperspectral Sensor Desis on the Multi-Payload Platform Muses Installed on the Iss

Müller, Rupert; Avbelj, J.; Carmona, E.; Eckardt, Andreas; Gerasch, B.; Graham, L.; Günther, Burghardt; Heiden, Uta; Ickes, Jack; Kerr, G.; Knodt, Uwe; Krutz, David; Krawczyk, H.; Makarau, A.; Miller, R.; Perkins, R.; Walter, Ingo

doi:10.5194/isprsarchives-xli-b1-461-2016

Cited by 34 publications

(22 citation statements)

References 2 publications

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“…Table A1. Specifications of other EO hyperspectral sensors excluded from this application review due to not matching the defined criteria [196][197][198][199][200][201][202][203][204][205]. …”

Section: Conflicts Of Interestmentioning

confidence: 99%

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

et al. 2018

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In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi-and hyper-spectral data. This study, therefore, reviews 20 years of research and applications in satellite hyperspectral remote sensing through the analysis of Earth observation hyperspectral sensors' publications that cover the Sentinel-2 spectrum range: Hyperion, TianGong-1, PRISMA, HISUI, EnMAP, Shalom, HyspIRI and HypXIM. More specifically, this study (i) brings face to face past and future hyperspectral sensors' applications with Sentinel-2's and (ii) analyzes the applications' requirements in terms of spatial and temporal resolutions. Eight main application topics were analyzed including vegetation, agriculture, soil, geology, urban, land use, water resources and disaster. Medium spatial resolution, long revisit time and low signal-to-noise ratio in the short-wave infrared of some hyperspectral sensors were highlighted as major limitations for some applications compared to the Sentinel-2 system. However, these constraints mainly concerned past hyperspectral sensors, while they will probably be overcome by forthcoming instruments. Therefore, this study is putting forward the compatibility of hyperspectral sensors and Sentinel-2 systems for resolution enhancement techniques in order to increase the panel of hyperspectral uses.

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Section: Conflicts Of Interestmentioning

confidence: 99%

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

et al. 2018

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“…The first instrument on the MUSES platform is DESIS. It is a hyperspectral instrument, jointly developed by the German Aerospace Center (DLR) and Teledyne Brown Engineering [18]. In addition to the many objectives and applications of hyperspectral missions mentioned above, DESIS is opening up further special fields of application.…”

Section: Desismentioning

confidence: 99%

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

Krutz¹,

Müller²,

Knodt

et al. 2019

Sensors

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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.

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“…In recent years, hyperspectral remote sensing has developed rapidly. For example, Italy launched the PRecursore IperSpettrale della Missione Applicativa (PRISMA) earth observation satellite in March 2019 [1], Japan launched the Hyperspectral Imager Suite (HISUI) hyperspectral satellite sensor in 2019 [2], India launched the ISRO's Hyperspectral Imaging Satellite (HYSIS) hyperspectral satellite in 2018 [3], and Germany launched the DLR Earth Sensing Imaging Spectrometer (DESIS) hyperspectral satellite in 2018 and are planning to launch the Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite in 2020 [4,5]. The development of the hyperspectral satellite field will bring about new requirements for image processing.…”

Section: Introductionmentioning

confidence: 99%

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

et al. 2020

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The China GaoFen-5 (GF-5) satellite sensor, which was launched in 2018, collects hyperspectral data with 330 spectral bands, a 30 m spatial resolution, and 60 km swath width. Its competitive advantages compared to other on-orbit or planned sensors are its number of bands, spectral resolution, and swath width. Unfortunately, its applications may be undermined by its relatively low spatial resolution. Therefore, the data fusion of GF-5 with high spatial resolution multispectral data is required to further enhance its spatial resolution while preserving its spectral fidelity. This paper conducted a comprehensive evaluation study of fusing GF-5 hyperspectral data with three typical multispectral data sources (i.e., GF-1, GF-2 and Sentinel-2A (S2A)), based on quantitative metrics, classification accuracy, and computational efficiency. Datasets on three study areas of China were utilized to design numerous experiments, and the performances of nine state-of-the-art fusion methods were compared. Experimental results show that LANARAS (this method was proposed by lanaras et al.), Adaptive Gram–Schmidt (GSA), and modulation transfer function (MTF)-generalized Laplacian pyramid (GLP) methods are more suitable for fusing GF-5 with GF-1 data, MTF-GLP and GSA methods are recommended for fusing GF-5 with GF-2 data, and GSA and smoothing filtered-based intensity modulation (SFIM) can be used to fuse GF-5 with S2A data.

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The New Hyperspectral Sensor Desis on the Multi-Payload Platform Muses Installed on the Iss

Cited by 34 publications

References 2 publications

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

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

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

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