Prof. Xavier Neyt Profile

Prof. Xavier Neyt

Professor at Royal Military Academy

SPIE Involvement:

Editor | Author

Proceedings Article | 14 October 2015 Paper

Assessment of the corrected CMOD6 GMF using scatterometer data

Anis Elyouncha, Xavier Neyt, Ad Stoffelen, Jeroen Verspeek

Proceedings Volume 9638, 963803 (2015) https://doi.org/10.1117/12.2195727

KEYWORDS: Backscatter, Calibration, Network on a chip, Radar, Data modeling, Scatterometry, Transponders, Wind measurement, Scatter measurement, Solids

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Proceedings Article | 28 October 2014 Paper

Comparison of the spatial and radiometric resolution of ERS and Metop C-band radars

Anis Elyouncha, Xavier Neyt

Proceedings Volume 9240, 92400L (2014) https://doi.org/10.1117/12.2068190

KEYWORDS: Radar, Antennas, Signal to noise ratio, Spatial resolution, Radiometric resolution, Optical filters, Satellites, Backscatter, Spatial filters, Linear filtering

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Proceedings Article | 10 October 2014 Paper

Analysis of C-band spaceborne scatterometer thermal noise

Anis Elyouncha, Xavier Neyt

Proceedings Volume 9240, 924006 (2014) https://doi.org/10.1117/12.2068172

KEYWORDS: Interference (communication), Antennas, Signal to noise ratio, Receivers, Backscatter, Signal processing, Radiometry, Radar, Scatter measurement, Optical filters

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Proceedings Article | 16 October 2013 Paper

Inter-calibration of Metop-A and Metop-B scatterometers using ocean measurements

Anis Elyouncha, Xavier Neyt

Proceedings Volume 8888, 888806 (2013) https://doi.org/10.1117/12.2033927

KEYWORDS: Calibration, Backscatter, Antennas, Radar, Scatter measurement, Satellites, Data modeling, Computer simulations, Fluctuations and noise, Sensors

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Proceedings Article | 19 October 2012 Paper

A method for cross-comparison of scatterometer data using natural distributed targets: application to ERS-1 and ERS-2 data during the tandem mission

Anis Elyouncha, Xavier Neyt

Proceedings Volume 8532, 85320O (2012) https://doi.org/10.1117/12.975218

KEYWORDS: Backscatter, Calibration, Satellites, Antennas, Data modeling, Instrument modeling, Computer simulations, Distance measurement, Remote sensing, Radar

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Proceedings Article | 19 October 2012 Paper

Contrast based band selection for optimized weathered oil detection in hyperspectral images

Florian Levaux, Charles Bostater Jr., Xavier Neyt

Proceedings Volume 8532, 85321D (2012) https://doi.org/10.1117/12.2007322

KEYWORDS: Hyperspectral imaging, Bidirectional reflectance transmission function, Sensors, Reflectivity, Target detection, Absorption, Backscatter, Surveillance, Imaging systems, Image filtering

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Hyperspectral imagery offers unique benefits for detection of land and water features due to the information contained in reflectance signatures such as the bi-directional reflectance distribution function or BRDF. The reflectance signature directly shows the relative absorption and backscattering features of targets. These features can be very useful in shoreline monitoring or surveillance applications, for example to detect weathered oil. In real-time detection applications, processing of hyperspectral data can be an important tool and Optimal band selection is thus important in real time applications in order to select the essential bands using the absorption and backscatter information. In the present paper, band selection is based upon the optimization of target detection using contrast algorithms. The common definition of the contrast (using only one band out of all possible combinations available within a hyperspectral image) is generalized in order to consider all the possible combinations of wavelength dependent contrasts using hyperspectral images. The inflection (defined here as an approximation of the second derivative) is also used in order to enhance the variations in the reflectance spectra as well as in the contrast spectrua in order to assist in optimal band selection. The results of the selection in term of target detection (false alarms and missed detection) are also compared with a previous method to perform feature detection, namely the matched filter. In this paper, imagery is acquired using a pushbroom hyperspectral sensor mounted at the bow of a small vessel. The sensor is mechanically rotated using an optical rotation stage. This opto-mechanical scanning system produces hyperspectral images with pixel sizes on the order of mm to cm scales, depending upon the distance between the sensor and the shoreline being monitored. The motion of the platform during the acquisition induces distortions in the collected HSI imagery. It is therefore necessary to apply a motion correction to the imagery. In this paper, imagery is corrected for the pitching motion of a vessel, which causes most of the deformation when the vessel is anchored at 2 points (bow and stern) during the acquisition of the hyperspectral imagry.

Proceedings Article | 19 October 2011 Paper

Airborne imaging sensors for environmental monitoring & surveillance in support of oil spills and recovery efforts

Charles Bostater, James Jones, Heather Frystacky, Gaelle Coppin, Florian Leavaux, Xavier Neyt

Proceedings Volume 8175, 81750B (2011) https://doi.org/10.1117/12.901231

KEYWORDS: Sensors, Hyperspectral imaging, Bidirectional reflectance transmission function, Imaging systems, Vegetation, Cameras, Global Positioning System, Calibration, Reflectivity, Filtering (signal processing)

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Collection of pushbroom sensor imagery from a mobile platform requires corrections using inertial measurement units (IMU's) and DGPS in order to create useable imagery for environmental monitoring and surveillance of shorelines in freshwater systems, coastal littoral zones and harbor areas. This paper describes a suite of imaging systems used during collection of hyperspectral imagery in northern Florida panhandle and Gulf of Mexico airborne missions to detect weathered oil in coastal littoral zones. Underlying concepts of pushbroom imagery, the needed corrections for directional changes using DGPS and corrections for platform yaw, pitch, and roll using IMU data is described as well as the development and application of optimal band and spectral regions associated with weathered oil. Pushbroom sensor and frame camera data collected in response to the recent Gulf of Mexico oil spill disaster is presented as the scenario documenting environmental monitoring and surveillance techniques using mobile sensing platforms. Data was acquired during the months of February, March, April and May of 2011. The low altitude airborne systems include a temperature stabilized hyperspectral imaging system capable of up to 1024 spectral channels and 1376 spatial across track pixels flown from 3,000 to 4,500 feet altitudes. The hyperspectral imaging system is collocated with a full resolution high definition video recorder for simultaneous HD video imagery, a 12.3 megapixel digital, a mapping camera using 9 inch film types that yields scanned aerial imagery with approximately 22,200 by 22,200 pixel multispectral imagery (~255 megapixel RGB multispectral images in order to conduct for spectral-spatial sharpening of fused multispectral, hyperspectral imagery. Two high spectral (252 channels) and radiometric sensitivity solid state spectrographs are used for collecting upwelling radiance (sub-meter pixels) with downwelling irradiance fiber optic attachment. These sensors are utilized for cross calibration and independent acquisition of ground or water reflectance signatures and for calculation of the bi-directional reflectance distribution function (BRDF). Methods are demonstrated for selecting optimal spectral regions and bands for discrimination, detection and characterization of weathered oil in the Northern Gulf of Mexico waters and littoral zones in response to the Deepwater Horizon oil spill disaster. The techniques allow for the use of sun and sky glint regions in imagery to identify water surface wave field characteristics as well as oil slicks. The systems described provide unique data sets for remote sensing algorithm development and future testing of radiative transfer models useful in studying weathered oil fate, distribution and extent.

Proceedings Article | 10 September 2009 Paper

Radiometric cross-calibration of spaceborne scatterometers: first results

Anis Elyouncha, Xavier Neyt, Marc Acheroy

Proceedings Volume 7473, 74730B (2009) https://doi.org/10.1117/12.832652

KEYWORDS: Backscatter, Calibration, Satellites, Scatter measurement, Data modeling, Space operations, Radar, Clouds, Sensors, Data centers

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The main application of a scatterometer is the determination of the wind speed and direction at the sea surface. This is achieved by measuring the radar backscattering coefficient in three different directions and inverting these measurements using a geophysical model function (GMF). The scientific value of the data is directly related to the quality of the radiometric calibration. There are currently two european C-band scatterometers operating, one on-board the ERS-2 spacecraft launched in 1995 and the other on-board METOP-A, launched in 2006. The similarity of the two scatterometers is an opportunity to ensure the continuity of more than 15 years of global scatterometer measurements. To achieve the consistency of the backscattering coefficients data sets, required for long-term climate studies, an accurate cross-calibration is vital. The cross-calibration is made possible since the two spacecrafts operate simultaneously from 2006 up to now. As the backscattering coefficients measured by the scatterometers depend on acquisition time, location on the ground and on the geometry of the measurements (incidence and look angle), a direct comparison of measurements made by both instruments is practically impossible. In particular cases, models can be used to cope with measurement differences. On the rain forest, assumed to be time-invariant, homogeneous and isotropic, the backscattering coefficient depends only on the incidence angle, and the constant gamma model can be used to cope with the incidence angle effects. On some ice covered areas (e.g. Greenland and Antarctica), assuming that the ice surface is isotropic, the ice line model can be used. It is a function of incidence angle and ice age and depends on the location. On the ocean, which is inherently not stable in time, the CMOD5 GMF is used. CMOD5 relates the observed backscatter to the geophysical parameters which are the wind speed and wind direction. Using the last model, measurement biases can be assessed making simultaneous observations unnecessary.

Proceedings Article | 13 October 2006 Paper

Analysis of the impact of ASCAT's pulse compression

Xavier Neyt, Nicolas Manise, Marc Acheroy

Proceedings Volume 6360, 63600E (2006) https://doi.org/10.1117/12.690180

KEYWORDS: Doppler effect, Radar, Error analysis, Signal processing, Antennas, Calibration, Space operations, Satellites, Signal to noise ratio, Backscatter

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Proceedings Article | 20 October 2005 Paper

Calibration strategy for ERS scatterometer data reprocessing

N. Manise, X. Neyt, M. Acheroy

Proceedings Volume 5977, 59770A (2005) https://doi.org/10.1117/12.627708

KEYWORDS: Calibration, Antennas, Transponders, Data modeling, Satellites, Backscatter, Radar, Gyroscopes, Solids, Data acquisition

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Proceedings Article | 19 October 2005 Paper

Enhanced neural-network based sea/ice discrimination using ERS scatterometer data

Xavier Neyt, Nicolas Manise, Marc Acheroy

Proceedings Volume 5977, 597707 (2005) https://doi.org/10.1117/12.627720

KEYWORDS: Backscatter, Anisotropy, Wind measurement, Neural networks, Distance measurement, Satellites, Neurons, Antennas, Information fusion, Scatterometry

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Proceedings Article | 14 February 2003 Paper

Scatterometer ground processing review for gyroless operations

Xavier Neyt, Pauline Pettiaux, Marc Acheroy

Proceedings Volume 4880, (2003) https://doi.org/10.1117/12.463248

KEYWORDS: Doppler effect, Linear filtering, Electronic filtering, Space operations, Spatial resolution, Filtering (signal processing), Antennas, Signal to noise ratio, Interference (communication), Satellites

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Proceedings Article | 14 February 2003 Paper

Validation of the ERS-2 scatterometer ground processor upgrade

Pauline Pettiaux, Xavier Neyt, Marc Acheroy

Proceedings Volume 4880, (2003) https://doi.org/10.1117/12.463250

KEYWORDS: Space operations, Data acquisition, Doppler effect, Backscatter, Calibration, Antennas, Satellites, Error analysis, Data processing, Gyroscopes

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Showing 5 of 13 publications

SPIE Conference Volume | 7 November 2023

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2023

SPIE Conference Volume | 9 November 2022

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2022

SPIE Conference Volume | 15 October 2021

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2021

SPIE Conference Volume | 9 October 2020

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2020

SPIE Conference Volume | 2 December 2019

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2019

Showing 5 of 18 publications

Conference Committee Involvement (18)

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2023

4 September 2023 | Amsterdam, Netherlands

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2022

6 September 2022 | Berlin, Germany

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2021

13 September 2021 | Online Only, Spain

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2020

22 September 2020 | Online Only, United Kingdom

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2019

9 September 2019 | Strasbourg, France

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018

10 September 2018 | Berlin, Germany

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2017

11 September 2017 | Warsaw, Poland

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2016

26 September 2016 | Edinburgh, United Kingdom

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2015

23 September 2015 | Toulouse, France

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2014

24 September 2014 | Amsterdam, Netherlands

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2013

24 September 2013 | Dresden, Germany

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2012

26 September 2012 | Edinburgh, United Kingdom

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2011

21 September 2011 | Prague, Czech Republic

Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2010

22 September 2010 | Toulouse, France

Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2009

31 August 2009 | Berlin, Germany

Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2008

15 September 2008 | Cardiff, Wales, United Kingdom

Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2007

17 September 2007 | Florence, Italy

Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2006

11 September 2006 | Stockholm, Sweden

Showing 5 of 18 Conference Committees

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