Tracking both structure and motion of nonrigid objects from monocular images is an important problem in vision. In this paper, a hierarchical method which integrates local analysis (that recovers small details) and global analysis (that appropriately limits possible nonrigid behaviors) is developed to recover dense depth values and nonrigid motion from a sequence of 2D satellite cloud images without any prior knowledge of point correspondences. This problem is challenging not only due to the absence of correspondence information but also due to the lack of depth cues in the 2D cloud images (scaled orthographic projection). In our method, the cloud images are segmented into several small regions and local analysis is performed for each region. A recursive algorithm is proposed to integrate local analysis with appropriate global fluid model constraints, based on which a structure and motion analysis system, SMAS, is developed. We believe that this is the first reported system in estimating dense structure and nonrigid motion under scaled orthographic views using fluid model constraints. Experiments on cloud image sequences captured by meteorological satellites (GOES-8 and GOES-9) have been performed using our system, along with their validation and analyses. Both structure and 3D motion correspondences are estimated to subpixel accuracy. Our results are very encouraging and have many potential applications in earth and space sciences, especially in cloud models for weather prediction.

Time-varying multispectral observations of clouds from meteorological satellites are used to estimate cloud-top heights (structure) and cloud winds (semi-fluid motion). Stereo image pairs over several time steps were acquired by two geostationary satellites with synchronized scanning instruments. Cloud-top height estimation from these image pairs is performed using an improved automatic stereo analysis algorithm on a massively parallel Maspar computer with 16 K processors. A new category of motion behavior known as semi-fluid motion is described for modeling cloud motions and an automatic algorithm for extracting semi-fluid motion is developed to track cloud winds. The time sequential dense estimates of cloud-top height depth maps in conjunction with intensity data are used to estimate local semi-fluid motion parameters for cloud tracking. Both stereo disparities and motion correspondences are estimated to sub-pixel accuracy. The Interactive Image SpreadSheet (IISS) is a new versatile visualization tool that was enhanced to analyze and visualize the results of the stereo analysis and semi-fluid motion estimation algorithms. Experimental results using time-varying data of the visible channel from two satellites in geosynchronous orbit is presented for the Hurricane Frederic.>

A system has been developed to display color stereographic time-lapsed sequences of outputs of three-dimensional atmospheric models. Contour lines and wind vectors derived from a model can be overlaid on a geostationary satellite image or displayed with map boundaries. The colors of these graphics can be changed interactively to highlight any desired feature or aid in interpreting relationships between parameters.