Automatic and Generic Mosaicing of Multisensor Images: An Application to Pleiades Hr

Bignalet-Cazalet, F.; Baillarin, Simon; Panem, C.

doi:10.5194/isprsarchives-xxxix-b1-509-2012

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…Sensor based methods can be split into two main categories: optical and Synthetic Aperture Radar (SAR) imagery. In case of optical imagery, the most critical issues to be addressed are the inhomogeneous radiometry related to different illumination conditions across overlapping scenes, different content such as changed agriculture or forestry and undesired objects such as clouds and shadows in the images [4]. For SAR data, several factors affect the geometric and radiometric quality of the data like terrain distortions, changes in look angles or in look directions.…”

Section: Introductionmentioning

confidence: 99%

Mosaicking Copernicus Sentinel-1 Data at Global Scale

Syrris

Corbane

Pesaresi

et al. 2020

IEEE Trans. Big Data

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This paper presents a processing chain for handling big volume of remotely sensing data for generating wide extent mosaics. More specifically, the data under consideration are level-1 ground range detected Sentinel-1 products with dual polarisation (VV+VH or HH+HV). Two approaches for a) distribution discretization accompanied by false color composition and b) image rendering and mosaicking are proposed. While these two components are necessary constituents of the presented mosaicking workflow, they can operate independently of each other. The design of the processing chain satisfies three objectives: i) contrasting derivative products of the input Sentinel-1 imagery such as the Global Human Settlement Layer, ii) adapting on a high-throughput computing system for fast execution, and iii) allowing potential extensions to more complex applications such as the image classification. Fast processing, process automation, incremental adjustment and information distinction are the main advantages of the proposed method. Elaboration and focus on these features are carried out during the presentation of the results.

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Section: Introductionmentioning

confidence: 99%

Mosaicking Copernicus Sentinel-1 Data at Global Scale

Syrris

Corbane

Pesaresi

et al. 2020

IEEE Trans. Big Data

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“…It is assumed that the co-registration error between the products is in the order of lower than one pixel. However, the combination of images still has to account for several challenges, e.g., inhomogeneous radiometry such as illumination conditions, different content such as changed agriculture or forestry, and undesired objects such as clouds in the images (Bignalet-Cazalet et al 2012). Two major strategies for combining data are typically distinguished.…”

Section: Mosaicking Of High-resolution Multi-spectral Imagesmentioning

confidence: 99%

Towards Fast Morphological Mosaicking of High-Resolution Multi-Spectral Products – On Improvements of Seamlines

Storch¹,

Fischer²,

Fast³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The complex process of fully automatically establishing seamlines for the fast production of high-quality mosaics with high-amount of high-resolution multi-spectral images is detailed and improved in this paper. The algorithm is analyzed and a quasi-linear runtime in the number of considered pixels is proven for all situations. For typical situations the storage is even essentially smaller from a complexity theoretical perspective. Improvements from algorithm practical perspective are specified, too. The influence of different methods for the determination of seamlines based on gradients is investigated in detail for three Sentinel-2 products. The studied techniques cover well-known ones normally based on a single band. But also more sophisticated techniques based on multiple bands or even taking additional external geo-information data are taken into account. Based on the results a larger area covered by Image2006 orthorectified products with data of the Resourcesat-1 mission is regarded. The feasibility of applying advanced subordinated methods for improving the mosaic such as radiometric harmonization is examined. This also illustrates the robustness of the improved seamline determination approaches.

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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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Automatic and Generic Mosaicing of Multisensor Images: An Application to Pleiades Hr

Cited by 8 publications

References 3 publications

Mosaicking Copernicus Sentinel-1 Data at Global Scale

Mosaicking Copernicus Sentinel-1 Data at Global Scale

Towards Fast Morphological Mosaicking of High-Resolution Multi-Spectral Products – On Improvements of Seamlines

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

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