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Remote sensing radar techniques provide highly detailed imaging. Nevertheless, radar images do not offer directly retrievable representations of shape within the scene. Therefore, shape reconstruction from radar typically relies on applying post-processing computer vision techniques, originally designed for optical images, to radar imaging products. Shape reconstruction directly from raw data would be desirable in many applications, e.g. in computer vision and robotics. In this perspective, inversion seems an attractive approach. Nevertheless, inversion has seldom been attempted in the radar context, as high frequency signals lead to energy functionals dominated by tightly packed narrow local minima. In this paper, we take the first step in developing a framework in which radar signals and images can be jointly used for shape reconstruction. In particular, we investigate the feasibility of shape reconstruction by inversion of pulse-compressed radar signals alone, collected at sparse locations. Motivated by geometric methods that have matured within the fields of image processing and computer vision, we pose the problem in a variational context obtaining a partial differential equation for the evolution of an initial shape towards the shape-reflectivity combination that best reproduces the data. While doing so, we highlight several non-obvious difficulties encountered and discuss how to surpass them. We illustrate the potential of this approach through three simulated examples and discuss several implementation choices, including boundary conditions, reflectivity estimation, and radiative models. The success of our simulations shows that this variational approach can naturally accommodate radar inversion and has the potential for further expansion towards active surfaces and level set applications, where we believe it will naturally complement current applications with optical images. [ABSTRACT FROM AUTHOR]

A process is described for computation of bathymetry in and near the surf zone, from spatially varying celerity and breakpoint location data. The procedure involves the use of three submodels: (1) a wave shoaling model (outside of the surf zone); (2) a wave breaking model (defining the offshore boundary of the surf zone); and (3) a wave dissipation model (inside the surf zone). Influence of wave amplitude on the wave dispersion relation and celerity is included. Output includes wave height and water depth throughout the domain. In the application described here, oblique digital video served as the initial data source, although the model could be applied to data derived from other sources. Results are compared with data recorded by in situ sensors and beach profile survey data acquired by traditional means. Results suggest that water depths can be computed within 15% normalized error (equally, less than 0.1 m in biased depth error) for in and near the surf zone characterized by high wave nonlinearity. [ABSTRACT FROM AUTHOR]