Airborne Imaging Spectrometer HySpex

Köhler, Claas

doi:10.17815/jlsrf-2-151

Cited by 24 publications

(22 citation statements)

References 9 publications

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“…2). At an altitude of 2666 m, the spatial resolution of the sensor is 1 m [31]. It provides 160 spectral bands between 400 nm and 1000 nm at 3.6 nm intervals.…”

Section: B Datamentioning

confidence: 99%

Benefit of the Potential Future Hyperspectral Satellite Sensor (BIODIVERSITY) for Improving the Determination of Water Column and Seabed Features in Coastal Zones

Minghelli

Vadakke-Chanat

Chami

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Most of the studies dealing with seabed mapping from hyperspectral images have been carried out using airborne data although hyperspectral satellite sensors have already been or are planned to be launched for the near future (HICO ENMAP or BIODIVERSITY). The objective of this study is to evaluate the benefit of a BIODIVERSITY-like sensor to determine the biooptical properties of the water column, namely the Chlorophyll-a concentration, the Suspended Particulate Matter concentration, the absorption coefficient of the Colored Dissolved Organic Matter, the bathymetry and the composition of the seabed, according to its spatial resolution and spectral resolution and its Signal to Noise Ratio (SNR). For this purpose, radiative transfer simulations are analyzed together with remote sensing hyperspectral airborne data (HYSPEX) acquired above the Porquerolles Island (France). The retrieval performance of all inwater and seabed parameters derived from the inversion of BIODIVERSITY-like data is compared with the performance obtained using ENMAP and HICO spatial and radiometric specifications. It is shown that a BIODIVERSITY-like sensor significantly improves the estimation performance of the water column parameters. Furthermore, BIODIVERSITY-like sensor is highly appropriate for seabed mapping when bottom pixels are composed of pure material (e.g., Sand or Posidonia) in shallow waters when seabed depth is less than 10 m. Conversely, the performance of the inversion deteriorates when seabed pixels are composed of mixed materials (e.g., Sand mixed with Posidonia). It is also shown that the concentration of chlorophyll, SPM and CDOM absorption are less sensitive to noise level than depth and seabed abundance.

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“…2). At an altitude of 2666 m, the spatial resolution of the sensor is 1 m [31]. It provides 160 spectral bands between 400 nm and 1000 nm at 3.6 nm intervals.…”

Section: B Datamentioning

confidence: 99%

Benefit of the Potential Future Hyperspectral Satellite Sensor (BIODIVERSITY) for Improving the Determination of Water Column and Seabed Features in Coastal Zones

Minghelli

Vadakke-Chanat

Chami

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The spatial resolution is 0.5 m. These images cover very different landscapes: forest, sea, beach, urban area, parking lot, airport, harbour, and so forth. [46]) within the context of the Hypex project, managed and funded by the DGA (Direction Générale de l'Armement). The HySpex sensor is composed of two cameras-a VNIR camera and a SWIR camera.…”

Section: Odin Imagesmentioning

confidence: 99%

Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria

Alakian

Achard

2020

Remote Sensing

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A classification method of hyperspectral reflectance images named CHRIPS (Classification of Hyperspectral Reflectance Images with Physical and Statistical criteria) is presented. This method aims at classifying each pixel from a given set of thirteen classes: unidentified dark surface, water, plastic matter, carbonate, clay, vegetation (dark green, dense green, sparse green, stressed), house roof/tile , asphalt, vehicle/paint/metal surface and non-carbonated gravel. Each class is characterized by physical criteria (detection of specific absorptions or shape features) or statistical criteria (use of dedicated spectral indices) over spectral reflectance. CHRIPS input is a hyperspectral reflectance image covering the spectral range [400–2500 nm]. The presented method has four advantages, namely: (i) is robust in transfer, class identification is based on criteria that are not very sensitive to sensor type; (ii) does not require training, criteria are pre-defined; (iii) includes a reject class, this class reduces misclassifications; (iv) high precision and recall, F 1 score is generally above 0.9 in our test. As the number of classes is limited, CHRIPS could be used in combination with other classification algorithms able to process the reject class in order to decrease the number of unclassified pixels.

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“…As is the case for every airborne sensor, the spatial resolution is depending on the flight height. More information on the HySpex sensors can be found in [1].…”

Section: The Sensors 21 Hyspexmentioning

confidence: 99%

Differences and Similarities in the Processing of Airborne and Spaceborne Hyperspectral Data Shown on HySpex and EnMap Processing Chains

Schneider

Baumgartner

Schwind

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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When working with hyperspectral data, it is very important, that the data is properly pre-processed in terms of systematic radiometric and spectral correction, geometric correction as well as atmospheric correction. Airborne and spaceborne sensors show some similarities regarding the processing, but also some differences. In this paper, these similarities and differences are discussed on the example of the HySpex processing chain in the generic processing environment Catena and the EnMAP processor that is currently developed at DLR. The paper presents the different sensors and their properties and gives an overview of the different workflows and the used algorithms.

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Airborne Imaging Spectrometer HySpex

Cited by 24 publications

References 9 publications

Benefit of the Potential Future Hyperspectral Satellite Sensor (BIODIVERSITY) for Improving the Determination of Water Column and Seabed Features in Coastal Zones

Benefit of the Potential Future Hyperspectral Satellite Sensor (BIODIVERSITY) for Improving the Determination of Water Column and Seabed Features in Coastal Zones

Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria

Differences and Similarities in the Processing of Airborne and Spaceborne Hyperspectral Data Shown on HySpex and EnMap Processing Chains

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