Water Motion Analysis in Sst Images Using Least Squares Methods

This paper presents an optimal solution to water motion in satellite images. Since temperature patterns are suitable tracers in water motion, Sea Surface Temperature (SST) images of Caspian Sea taken by MODIS sensor on board Terra satellite have been used in this study. Two daily SST images with 24 hours time interval are used as input data. Computation of templates correspondence between pairs of images is crucial within motion algorithms using non-rigid body objects. Image matching methods have been applied to estimate water body motion within the two SST images in this study. The least squares matching technique, as a flexible technique for most data matching problems, offers an optimal spatial solution for the motion estimation. The algorithm allows for simultaneous local (i.e. template) radiometric correction and local geometrical image orientation estimation. Actually, the correspondence between two image templates is modeled both geometrically and radiometrically. The next method to extract water motion is hierarchical Lucas and Kanade method that implements weighted least squares fit of local first-order optical flow constraints in each spatial neighborhood. This method by using coarse-to-fine strategy to track motion in Gaussian pyramids of SST image finds water current. This method allows the detection of large motions in coarse resolution layer and gradually leads to more precise result in finer layers. The methods used in this study, has presented more efficient and robust solution compared to the traditional motion estimation schemes to extract water currents.