Motion Estimation of Sparse, Remotely-sensed Fields

Sea surface temperature (SST) can be estimated from remotely-sensed images. Because of the sparsity of the available observation it is ideal to do estimation using dynamic methods (such as Kalman filtering). To model dynamics of SST accurately we need to know motion of sea current. The traditional video motion estimation problem is straightforward, in some ways, because there are so few constraints. That is, the motion vectors are pretty much arbitrary, and successive image frames are densely pixellated, have the same number of pixels, with similar noise statistics. However there are many motion estimation problems, particularly in the area of remote sensing, which do not share these properties. In this paper we investigate the problem of determining the motion field of the sea surface, based on infrared measurements of surface temperature. This problem is challenging in that only a subset of the whole domain is measured at each point in time; specifically, only a few stripes are imaged. In addition, because of clouds, the measured subset varies from time to time; in fact, some days absolutely nothing is imaged. The quality (level of noise) can also vary from pixel to pixel. Our research will be based on the following assumptions and observations: the motion field should be smooth and ideally divergence-free, i.e. the motion field is close to time-stationary. Based on these assumptions we choose to use optical flow method for this motion problem. We handle difticulty of data sparcity by pre-estimation to get a dense field. Pre-estimation can be refined by integrating this motion estimation result. Preliminary experiment result will be shown in the end. I . INTRODUCTION To track the changes of sea surface temperature (SST) we need to dynamically estimate temperature field from remotely-sensed images. For this purpose accurate model of sea dynamics is required. There are two main factors to concern in modelling SST: temperature diffusion and current motion. Diffusion model 500 t