Optical flow in radar images

Optical flow is one of the standard computer vision methods for computing motion vectors. The key idea is to derive motion vectors from intensity gradients and differences of subsequent images. This model is analogous to advection equations in meteorology which motivates testing the method for weather radar data. Also the computational simplicity of optical flow makes it an interesting alternative to autocorrelation based methods. Our final target is to apply motion vectors in nowcasting precipitation for 0–3 h. (One should keep in mind that motion of a precipitation area generally differs from Doppler measurable motion of droplets.) Related products typically exceed 1000×1000 pixels in size and the maximal allowed processing time is below a minute for deriving the motion vectors. In this paper, we briefly review the optical flow model and suggest techniques for fast, quality weighted computing in extraction of motion vectors. We emphasize weather radar data specific issues such as the discontinuity and multi-scale nature of precipitation. Nevertheless, our first experiments suggest that the proposed techniques are applicable also in a wider context of motion extraction problems.