Estimation of Dense Image Flow Fields in Fluids

• Published in: IEEE Transactions on Geoscience and Remote Sensing
• Main Authors: Larsen, Rasmus, Conradsen, Knut, Ersbøll, Bjarne Kjær
• Format: Article in Journal/Newspaper
• Language: English
• Published: 1998
• Subjects: Sea ice
• Online Access: https://orbit.dtu.dk/en/publications/c7b23ec7-7c52-4904-a2d6-d7867fac848b; https://doi.org/10.1109/36.655334; https://backend.orbit.dtu.dk/ws/files/4365342/Larsen.pdf

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.