Cloud motion estimation in SEVIRI image sequences

Abstract

Determination of atmospheric dynamic characteristics from remote sensing imagery is fundamental in weather and climate studies. The SEVIRI radiometer, on board the MSG, with its 12 bands and 15 minutes sensing capability provides an important amount of information for cloud tracking. In this work, we have first conducted a detailed evaluation of twelve region matching techniques in order to select those providing the best results. For this performance evaluation, databases of synthetic and real sequences have been used. Next, the best metrics have been incorporated in a new methodology that includes a preliminary stage that segments cloudy structures to initialize the optimum motion estimation parameters (template size and search window dimensions). Also a study region mask is generated to disable the application of the motion estimation algorithm in unreliable areas, thus, eliminating erroneous vectors and decreasing the computation times.