Attitude Assessment Using Pleiades-Hr Capabilities

Delevit, J. M.; Greslou, D.; Amberg, V.; Dechoz, Cécile; Lussy, F. de; Lebègue, Laurent; Latry, Christophe; Artigues, Stéphanie; Bernard, Laurent

doi:10.5194/isprsarchives-xxxix-b1-525-2012

Cited by 42 publications

(25 citation statements)

References 11 publications

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“…The single-track reverse imaging method, which is called auto-reverse, utilizes the spacecraft's high agility to rotate the spacecraft 180 degrees after imaging and to take a second image of the same spot [16]. This method looks quite promising and has several strengths over the GCP method.…”

Section: Introductionmentioning

confidence: 99%

“…Several interesting approaches were proposed for Pleiades-HR, such as single-track reverse imaging and star imaging, as well as the GCP method [16][17][18]. The single-track reverse imaging method, which is called auto-reverse, utilizes the spacecraft's high agility to rotate the spacecraft 180 degrees after imaging and to take a second image of the same spot [16].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is common for earth-observation satellite programs to calibrate the alignment during the initial commissioning period [2,3,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. However, it is difficult to obtain an accurate alignment because the alignment error is measured as the sum of various error sources [6].…”

Section: Introductionmentioning

confidence: 99%

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On-Orbit Camera Misalignment Estimation Framework and Its Application to Earth Observation Satellite

Lee

Shin

2015

Remote Sensing

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Despite the efforts for precise alignment of imaging sensors and attitude sensors before launch, the accuracy of pre-launch alignment is limited. The misalignment between attitude frame and camera frame is especially important as it is related to the localization error of the spacecraft, which is one of the essential factors of satellite image quality. In this paper, a framework for camera misalignment estimation is presented with its application to a high-resolution earth-observation satellite-Deimos-2. The framework intends to provide a solution for estimation and correction of the camera misalignment of a spacecraft, covering image acquisition planning to mathematical solution of camera misalignment. Considerations for effective image acquisition planning to obtain reliable results are discussed, followed by a detailed description on a practical method for extracting many GCPs automatically using reference ortho-photos. Patterns of localization errors that commonly occur due to the camera misalignment are also investigated. A mathematical model for camera misalignment estimation is described comprehensively. The results of simulation experiments showing the validity and accuracy of the misalignment estimation model are provided. The proposed framework was applied to Deimos-2. The real-world data and results from Deimos-2 are presented.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On-Orbit Camera Misalignment Estimation Framework and Its Application to Earth Observation Satellite

Lee

Shin

2015

Remote Sensing

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“…When the existing publications are reviewed, it can be summarized that Lamard et al (2004), Baudoin (2004), Lussy et al (2005), Baillarin et al (2006), Baillarin et al (2010), Panem et al (2012), Gleyzes et al (2012), and Boissin et al (2012) define the system, and present the specifications; Lussy et al (2005), J. M. , Lussy et al (2012), Greslou et al (2012) present the geometric properties; Lebegue et al (2010), Blanchet et al (2012), Lebègue et al (2012), Latry et al (2012), Fourest et al (2012) and Poli et al (2013) evaluate the radiometric quality; Delaunay et al (2008) mentions about the image compression algorithms; Bernard et al (2012) and Poli et al (2013) focuses on the DEM (Digital Elevation Model) generation and validation; Flamanc and Maillet (2005) study the 3D city modelling using the simulated images; Lachérade et al (2012) presents the in-flight calibration results, and Bignalet-Cazalet et al (2012) experience the mosaicking performances. Beaumet et.…”

Section: Literature Reviewmentioning

confidence: 99%

Georeferencing Accuracy Assessment of Pléiades 1A Images Using Rational Function Model

Topan

Taşkanat

Cam

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents the first experience of georeferencing accuracy analysis of Pléiades 1A mono images. The Pléiades Constellation has been founded by CNES (The Centre National d'Etudes Spatiales -National Centre for Space Studies) consisting of Pléiades 1A&1B, following the previous five sisters of SPOT series. CNES also organized a Pléiades Users Group following a world-wide invitation. The images investigated in this research were received as one of the member of this Group. A stereo-pair was evaluated on Zonguldak test field where the topography is mountainous and undulating overlapping urban, rural and forest landscapes. As a first experience, totally 22 ground control points (GCPs) already existing were marked on the images, and the bias compensated Rational Function Model (RFM) was carried out reaching ±0.8 pixel at the GCPs. The overall georeferencing accuracy was performed by the figure condition analysis (FCA), a new concept successfully applied to IKONOS, OrbView-3 and QuickBird images of the same test field. The range of figure condition is between ±0.3-2.7 pixels. These results were compared with the other images of three sensors mentioned above. Although a special GCP survey by GNSS has not been performed yet, these first results are satisfied for the highly accurate georeferencing of the Pléiades 1A images.

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“…The first is to use ground control points (GCPs) in the scene [10,11], and the second is to use high-performance attitude sensors to obtain the attitude information with high time and angular resolutions [12]. However, those methods that depend on accurate GCPs or high-performance attitude sensors are economically and technically infeasible for many on-orbit satellites.…”

Section: Introductionmentioning

confidence: 99%

Framework of Jitter Detection and Compensation for High Resolution Satellites

Tong

et al. 2014

Remote Sensing

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Attitude jitter is a common phenomenon in the application of high resolution satellites, which may result in large errors of geo-positioning and mapping accuracy. Therefore, it is critical to detect and compensate attitude jitter to explore the full geometric potential of high resolution satellites. In this paper, a framework of jitter detection and compensation for high resolution satellites is proposed and some preliminary investigation is performed. Three methods for jitter detection are presented as follows. (1) The first one is based on multispectral images using parallax between two different bands in the image; (2) The second is based on stereo images using rational polynomial coefficients (RPCs); (3) The third is based on panchromatic images employing orthorectification processing. Based on the calculated parallax maps, the frequency and amplitude of the detected jitter are obtained. Subsequently, two approaches for jitter compensation are conducted. (1) The first one is to conduct the compensation on image, which uses the derived parallax observations for resampling; (2) The second is to conduct the compensation on attitude data, which treats the influence of jitter on attitude as correction of charge-coupled device (CCD) viewing angles. Experiments with images from several satellites, such as ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiaometer), LRO (Lunar Reconnaissance 3945Orbiter) and ZY-3 (ZiYuan-3) demonstrate the promising performance and feasibility of the proposed framework.

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Attitude Assessment Using Pleiades-Hr Capabilities

Cited by 42 publications

References 11 publications

On-Orbit Camera Misalignment Estimation Framework and Its Application to Earth Observation Satellite

On-Orbit Camera Misalignment Estimation Framework and Its Application to Earth Observation Satellite

Georeferencing Accuracy Assessment of Pléiades 1A Images Using Rational Function Model

Framework of Jitter Detection and Compensation for High Resolution Satellites

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