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.
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.
@cite_6
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Salient object detection has recently witnessed substantial progress due to powerful features extracted using deep convolutional neural networks (CNNs). However, existing CNN-based methods operate at the patch level instead of the pixel level. Resulting saliency maps are typically blurry, especially near the boundary of salient objects. Furthermore, image patches are treated as independent samples even when they are overlapping, giving rise to significant redundancy in computation and storage. In this paper, we propose an end-to-end deep contrast network to overcome the aforementioned limitations. Our deep network consists of two complementary components, a pixel-level fully convolutional stream and a segment-wise spatial pooling stream. The first stream directly produces a saliency map with pixel-level accuracy from an input image. The second stream extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries. Finally, a fully connected CRF model can be optionally incorporated to improve spatial coherence and contour localization in the fused result from these two streams. Experimental results demonstrate that our deep model significantly improves the state of the art.