Singular features in sea surface temperature data
We propose to detect singular-features in order to generate an intelligent summary of high resolution spatio-temporal data that are obtained from satellite-based observations of the ocean. Toward this objective, we extend the Horn-Schunck model of flow field computation to incorporate incompressibility for tracking fluid motion. This is expressed as a zero-divergence constraint in the variational problem and an efficient multigrid implementation of it is introduced. Additionally, we show an effective localization of event features, such as vortices and saddle points, in the velocity field that can be used for subsequent abstraction, query and statistical analysis.