An algorithm to estimate motion from satellite imagery is presented. Dense displacement fields are computed from time-separated images of of significant convective activity using a Bayesian formulati on of the motion estimation problem. Ordinarily this motion estimation problem is ill-posed; there are far too many degrees of freedom than necessary to represent the motion. Therefore, some form of regularization becomes necessary and by imposing smoothness and non-divergence as desirable properties of the estimated displacement vector field, excellent solutions are obtained. Our approach provides a marked improvement over other methods in conventional � This material is supported in part by NSF ITR 0121182 and DDDAS 0540259.
@cite_6: Abstract A procedure for processing real world image sequences produced by relative translational motion between a sensor and environmental objects is presented. In this procedure, the determination of the direction of sensor translation is effectively combined with the determination of the displacement of image features and environmental depth. It requires no restrictions on the direction of motion, nor the location and shape of environmental objects. It has been applied successfully to real-world image sequences from several different task domains. The processing consists of two basic steps: feature extraction and search. The feature extraction process picks out small image areas which may correspond to distinguishing parts of environmental objects. The direction of translational motion is then found by a search which determines the image displacement paths along which a measure of feature mismatch is minimized for a set of features. The correct direction of translation will minimize this error measure and also determine the corresponding image displacement paths for which the extracted features match well.
In correlation based approaches @cite_6 , a region of interest (or patch) is identified in the first image and correlated within a search window in the second image. The location of the best match is then used to compute a displacement vector. When the input image or field is tiled, possibly overlapping, and regions of interest are extracted from each tile location, the result is velocimetry at regular intervals and is most commonly used for Particle Image Velocimetry (PIV). In certain instances it is useful to define interest-points or salient features around which to extract regions of interest. In particular, if the field has many areas with negligible spatial variability, then matches are undefined. As a quality control measure then, matching is restricted only to those regions of interest that have interesting variability, or interest points.