Computing advective velocities from satellite images of sea surface temperature

Wu, Qing; Pairman, D.; McNeill, Stephen; Barnes, E. J.

doi:10.1109/36.124227

Cited by 25 publications

(16 citation statements)

References 15 publications

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“…The velocity derived, as a measure of this transport is known as the advective velocity. The spatial resolution was one of the major constraints for achieving the desired accuracy (Garcia and Robinson 1989;Wu et al 1992). IRS-P4 OCM data has improved spatial resolution and distinct signatures of suspended sediments acting as tracer and hence they were analysed to derive the advective velocities.…”

Section: Resultsmentioning

confidence: 99%

“…The vector (p, q) represents a possible spatial displacement of the original pattern between the two images. The crosscorrelation matrix is formed as (Wu et al 1992):…”

Section: Implementation Of the Maximum Cross-correlation (Mcc) Pattermentioning

confidence: 99%

“…The pattern matching is performed on a pair of atmospherically corrected and geo-referenced sequential images by Maximum Cross-Correlation (MCC) technique. The work follows the technique developed by many investigators (La Violette 1984;Vastano and Borders 1984;Emery et al 1986;Kelly 1989;Garcia and Robinson 1989;Wu et al 1992) and has been applied on IRS-P4 OCM derived suspended sediment maps for the Indian region. The velocities retrieved are time-averaged velocities between the data sets used.…”

Section: Introductionmentioning

confidence: 99%

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Retrieval of sea surface velocities using sequential Ocean Colour Monitor (OCM) data

et al. 2002

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The Indian remote sensing satellite, IRS-P4 (Oceansat-I) launched on May 26th, 1999 carried two sensors on board, i.e., the Ocean Colour Monitor (OCM) and the Multi-frequency Scanning Microwave Radiometer (MSMR) dedicated for oceanographic research. Sequential data of IRS-P4 OCM has been analysed over parts of both east and west coast of India and a methodology to retrieve sea surface current velocities has been applied. The method is based on matching suspended sediment dispersion patterns, in sequential two time lapsed images. The pattern matching is performed on a pair of atmospherically corrected and geo-referenced sequential images by Maximum Cross-Correlation (MCC) technique. The MCC technique involves computing matrices of cross-correlation coefficients and identifying correlation peaks. The movement of the pattern can be calculated knowing the displacement of windows required to match patterns in successive images. The technique provides actual flow during a specified period by integrating both tidal and wind influences. The current velocities retrieved were compared with synchronous data collected along the east coast during the GSI cruise ST-133 of R.V. Samudra Kaustubh in January 2000. The current data were measured using the ocean current meter supplied by the Environmental Measurement and CONtrol (EMCON), Kochi available with the Geological Survey of India, Marine Wing. This current meter can measure direction and magnitude with an accuracy of ±5• and 2% respectively. The measurement accuracies with coefficient of determination (R 2 ) of 0.99, for both magnitude (cm.s −1 ) and direction (deg.) were achieved.

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Section: Resultsmentioning

confidence: 99%

“…The vector (p, q) represents a possible spatial displacement of the original pattern between the two images. The crosscorrelation matrix is formed as (Wu et al 1992):…”

Section: Implementation Of the Maximum Cross-correlation (Mcc) Pattermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Retrieval of sea surface velocities using sequential Ocean Colour Monitor (OCM) data

et al. 2002

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“…Les [2], [3], [4] pour comparer l'information contenue dans cette fenêtre à celle présente dans la même fenêtre mais placée sur une image différente. Le logiciel envisage alors tous les déplacements possibles de la première image et évalue, dans chaque cas, la corrélation existant entre les pixels des deux fenêtres en se basant sur le niveau de gris.…”

Section: • Mesure De La Vitesse Par Interunclassified

Utilisation des techniques d'imagerie pour la cartographie des vitesses à la surface d'une avalanche dense

Granada

Marco²,

Villemain

1995

La Houille Blanche

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Laboratoire d'Instrumentation de Micro-informatique et d'électroniqueL'étude de la dynamique des avalanches a donné lieu à de nombreux modèles théoriques et à quelques expérimenta-tions sur des sites avalancheux pour les valider. Nous proposons, dans cet article, une méthode d'analyse de films vidéo d'avalanches de neige dense avec pour objectif de réaliser une cartographie des vitesses à la surface de l'écoulement et de comparer ces résultats à ceux des modèles numériques. Les conditions des prises de vues sont très mal maîtrisées en raison du danger que représente l'avalanche pour l'opérateur. Le matériel de prise de vue est standard, car il ne peut être question d'utiliser des caméras à haute résolution ou à grande vitesse qui, par leur poids et leur sensibilité à la température, sont inadaptées. Les traitements doivent pouvoir donner des résultats sur des images extraites des documents vidéo ou films, obtenues par un matériel standard. Dans ces conditions, les images obtenues présentent deux difficultés pour leur traitement : la fréquence de 25 images/s conduit à un « flou de bougé » dû au temps de pose et, d'autre part, les formes et contrastes des images sont mal définis et instables au cours du temps. Cependant, des études ont été conduites avec succès sur des phénomènes semblables en météoro-logie ou en aérodynamique. L'analyse par autocorrélation et intercorrélation fournit des résultats corrects. Deux approches sont présentées ici. La première utilise l'intercorrélation entre images successives et a permis la représen-tation de l'évolution de la vitesse d'une portion de surface au cours du temps en quelques points de la surface de l'écoulement. La seconde utilise une méthode de filtrage et a permis d'obtenir la répartition des vitesses en tout point de surface de l'écoulement. Les résultats obtenus sont bidimensionnels car nous avons exclu de cette étude le problème de la restitution tridimensionnel des images qui sera traité ultérieurement.

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“…Scientists in the very active fields have focused their efforts on extending their methods [cf. Barron et al, 1994;Black and Anandan, 1996;Bruhn et al, 2005;Papenberg et al, 2006;Mercatini et al, 2010;Garcia and Robinson, 1989;Kamachi, 1989;Tokmakian et al, 1990;Emery et al, 1992;Holland and Yan, 1992;Wu, 1995;Wu and Pairman, 1995;Wu et al, 1992;Flores et al, 1995;Borzelli et al, 1999;Domingues et al, 2000;Prasad et al, 2002;Bowen et al, 2002;Moulin, 2004;Alberotanza and Zandonella, 2004;Dransfeld et al, 2006;Crocker et al, 2007;Matthews and Emery, 2009].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear inverse model for velocity estimation from an image sequence

Chen

2011

J. Geophys. Res.

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[1] Velocity estimation from an image sequence is one of the most challenging inverse problems in computer vision, geosciences, and remote sensing applications. In this paper a nonlinear model has been created for estimating motion field under the constraint of conservation of intensity. A linear differential form of heat or optical flow equation is replaced by a nonlinear temporal integral form of the intensity conservation constraint equation. Iterative equations with Gauss-Newton and Levenberg-Marguardt algorithms are formulated based on the nonlinear equations, velocity field modeling, and a nonlinear least squares model. An algorithm with progressive relaxation of the overconstraint to improve the performance of the velocity estimation is also proposed. The new estimator is benchmarked using a numerical simulation model. Both angular and magnitude error measurements based on the synthetic surface heat flow from the numerical model demonstrate that the performance of the new approach with the nonlinear model is much better than the results of using a linear model of heat or optical flow equation. Four sequences of NOAA Advanced Very High Resolution Radiometer (AVHRR) images taken in the New York Bight fields is also used to demonstrate the performance of the nonlinear inverse model, and the estimated velocity fields are compared with those measured with the Coastal Ocean Dynamics Radar array. The experimental results indicate that the nonlinear inverse model provides significant improvement over the linear inverse model for real AVHRR data sets.

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Computing advective velocities from satellite images of sea surface temperature

Cited by 25 publications

References 15 publications

Retrieval of sea surface velocities using sequential Ocean Colour Monitor (OCM) data

Retrieval of sea surface velocities using sequential Ocean Colour Monitor (OCM) data

Utilisation des techniques d'imagerie pour la cartographie des vitesses à la surface d'une avalanche dense

Nonlinear inverse model for velocity estimation from an image sequence

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