Estimation of Dense Image Flow Fields in Fluids

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    Abstract

    The estimation of flow fields from time sequences of satellite imagery has a number of important applications. For visualisation of cloud or sea ice movements in sequences of crude temporal sampling a satisfactory non-blurred temporal interpolation can be performed only when the flow field or an estimate there-of is known. Estimated flow fields in weather satellite imagery might also be used on an operational basis as inputs to short-term weather prediction. In this article we describe a method for the estimation of dense flow fields. Local measurements of motion are obtained by analysis of the local energy distribution, which is sampled using a set of 3-D spatio-temporal filters. The estimated local energy distribution also allows us to compute a confidence measure of the estimated local normal flow. The algorithm furthermore utilises Markovian random fields in order to integrate the local estimates of normal flows into a dense flow field using measures of spatial smoothness. To obtain smoothness we will constrain first order derivatives of the flow field. The performance of the algorithm is illustrated by the estimation of the flow fields corresponding to a sequence of Meteosat thermal images. The estimated flow fields are used in a temporal interpolation scheme.
    Original languageEnglish
    JournalGeoscience and Remote Sensing, IEEE Transactions on
    Volume36
    Issue number1
    Pages (from-to)256-264
    ISSN0196-2892
    DOIs
    Publication statusPublished - 1998

    Bibliographical note

    Copyright: 1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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