Motion estimation of sparse, remotely-sensed fields

TitleMotion estimation of sparse, remotely-sensed fields
Publication TypeConference Paper
Year of Publication2001
AuthorsJin, F., F. M. Khellah, P. Fieguth, and L. Winger
Conference Name14th Canadian Conference on Electrical and Computer Engineering
Conference LocationToronto
Keywordsdata sparcity, dense field, dynamic methods, geophysical signal processing, image sequences, infrared imaging, infrared measurements, Kalman filtering, motion estimation, motion field, oceanographic techniques, optical flow method, Remote Sensing, remotely-sensed images, sea current, sea surface temperature, sparse remotely-sensed fields, SST

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 the 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. The quality (level of noise) can also vary from pixel to pixel. Our research is 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 difficulty of data sparcity by pre-estimation to get a dense field. Pre-estimation can be refined by integrating this motion estimation result