SPOT5: system overview and image ground segment

Gleyzes, J.-P.; Meygret, Aimé; Fratter, C.; Panem, C.; Baillarin, Simon; Valorge, C.

doi:10.1109/igarss.2003.1293756

Cited by 18 publications

(15 citation statements)

References 4 publications

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“…Both technologies have their limitations and advantages [6] as open access datasets did not provide the required detailed information for studies at local scales [10]. The first stereoscopic images that allowed the extraction of DSM over large areas were provided by SPOT in 1986 [11]. Since then, several very high resolution (VHR) satellites with stereo and tri-stereo capabilities have been launched during the last decade based on the demand of higher accuracy and large area coverage for DSM production (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Validation of Pleiades Tri-Stereo DSM in Urban Areas

Panagiotakis

Chrysoulakis

Charalampopoulou

et al. 2018

IJGI

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Abstract:We present an accurate digital surface model (DSM) derived from high-resolution Pleiades-1B 0.5 m panchromatic tri-stereo images, covering an area of 400 km 2 over the Athens Metropolitan Area. Remote sensing and photogrammetry tools were applied, resulting in a 1 m × 1 m posting DSM over the study area. The accuracy of the produced DSM was evaluated against measured elevations by a differential Global Positioning System (d-GPS) and a reference DSM provided by the National Cadaster and Mapping Agency S.A. Different combinations of stereo and tri-stereo images were used and tested on the quality of the produced DSM. Results revealed that the DSM produced by the tri-stereo analysis has a root mean square error (RMSE) of 1.17 m in elevation, which lies within the best reported in the literature. On the other hand, DSMs derived by standard analysis of stereo-pairs from the same sensor were found to perform worse. Line profile data showed similar patterns between the reference and produced DSM. Pleiades tri-stereo high-quality DSM products have the necessary accuracy to support applications in the domains of urban planning, including climate change mitigation and adaptation, hydrological modelling, and natural hazards, being an important input for simulation models and morphological analysis at local scales.

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Section: Introductionmentioning

confidence: 99%

Validation of Pleiades Tri-Stereo DSM in Urban Areas

Panagiotakis

Chrysoulakis

Charalampopoulou

et al. 2018

IJGI

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“…This geometry depicts another common way of obtaining stereo images from what have come to be known as ''multi-line cameras'' (e.g. SPOT5 (Gleyzes et al, 2003)). This geometry has the advantage of obtaining the image pair with very small temporal displacements on the same orbital pass.…”

Section: Analysis Of Viewing Geometriesmentioning

confidence: 99%

“…An example is SPOT5 satellite (Gleyzes et al, 2003). For many systems, navigational accuracy has been greatly increased by the Global Positioning System (GPS).…”

Section: Introductionmentioning

confidence: 99%

Identifying degrees of freedom in pushbroom bundle adjustment

Jama

Lewis

Schinstock

2011

ISPRS Journal of Photogrammetry and Remote Sensing

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“…For assimilation or inverse modelling to models with a higher resolution than 50 km, the weighting could also be taken into account in the forward model for the XCH4. (Gleyzes et al, 2003). Amongst the 5 spectral bands of the High Resolution Geometric (HRG) instrument, it has a spectral band in the Short Wave InfraRed domain (1.55 to 1.7 µm) with a spatial resolution of 20 m. This band includes the MERLIN laser wavelength and, as we expect spectral variations of surface albedo to have rather low spectral variations, we use the "Spot SYSTEM SCENE level 1A" product (images using radiometric corrections, equivalent radiance in W.m -2 .Sr -1 .µm -1 ) as a proxy of surface reflectivity.…”

Section: 5mentioning

confidence: 99%

Averaging Bias Correction for the Future Space-borne Methane IPDA Lidar Mission MERLIN

Tellier¹,

Pierangelo²,

Wirth³

et al. 2018

Preprint

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Abstract. The CNES/DLR project MERLIN is a future IPDA lidar satellite mission that aims at measuring methane dry-air mixing ratio columns ( ) in order to improve surface flux estimates of this key greenhouse gas. To reach a 1 % relative 10 random error on measurements, MERLIN signal processing performs an averaging of data over 50 km along the satellite trajectory. This article discusses how to process this horizontal averaging in order to avoid the bias caused by the non-linearity of the measurement equation with measurements affected by random noise and horizontal geophysical variability. Three averaging schemes are presented: averaging of columns of , averaging of columns of Differential Absorption Optical Depth (DAOD) and averaging of signals. The three schemes are affected both by statistical and geophysical biases that are 15 discussed and compared and correction algorithms are developed for the three schemes. These algorithms are tested and their biases are compared on modeled scenes from real satellite data. To achieve the accuracy requirements that are limited to 0.2 % relative systematic error (for a reference value of 1780 ppb), we recommend performing the averaging of signals corrected from the statistical bias due to the measurement noise and from the geophysical bias mainly due to variations of methane optical depth and surface reflectivity along the averaging track. The proposed method is compliant with the mission relative 20 systematic error requirements dedicated to averaging algorithms of 0.07% (±1 ppb for = 1780 ppb) for all tested scenes and all tested ground reflectivity values. IntroductionMethane (CH4) is the second most important anthropogenic greenhouse gas after carbon dioxide (CO2) (IPCC, 2013) Section 2 gives an overview of the IPDA equations and MERLIN data processing. Section 3 defines and compares biases of several averaging schemes (described below) and suggests correction algorithms. Section 4 presents a comparative evaluation of these averaging schemes and associated bias correction procedures using modeled scenes based on real satellite data. And finally, in section 5, the results of the simulation are described and a "best approach" algorithm (i.e. the least biased on tested 10 scenes) is proposed for MERLIN processing chain. 2Overview of IPDA equations and the MERLIN processing chain MERLIN active measurement is based on a short pulse Integrated Path Differential Absorption (IPDA) lidar. The column content of methane between the satellite and the "hard" target (ground, vegetation, clouds...) is retrieved by measuring the

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SPOT5: system overview and image ground segment

Cited by 18 publications

References 4 publications

Validation of Pleiades Tri-Stereo DSM in Urban Areas

Validation of Pleiades Tri-Stereo DSM in Urban Areas

Identifying degrees of freedom in pushbroom bundle adjustment

Averaging Bias Correction for the Future Space-borne Methane IPDA Lidar Mission MERLIN

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