The estimation of flow fields from time sequences of satellite imagery has a number of important applications. For visualization 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 certainty measure of the estimated local flow. The algorithm furhtermore utilizes Markovian random fields in order to incorporate smoothness across the field. To obtain smothness we will constrain first as well as second 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.
- markov random fields
- optical flow
- local orientation