2,142 research outputs found
Spaceborne radar observations: A guide for Magellan radar-image analysis
Geologic analyses of spaceborne radar images of Earth are reviewed and summarized with respect to detecting, mapping, and interpreting impact craters, volcanic landforms, eolian and subsurface features, and tectonic landforms. Interpretations are illustrated mostly with Seasat synthetic aperture radar and shuttle-imaging-radar images. Analogies are drawn for the potential interpretation of radar images of Venus, with emphasis on the effects of variation in Magellan look angle with Venusian latitude. In each landform category, differences in feature perception and interpretive capability are related to variations in imaging geometry, spatial resolution, and wavelength of the imaging radar systems. Impact craters and other radially symmetrical features may show apparent bilateral symmetry parallel to the illumination vector at low look angles. The styles of eruption and the emplacement of major and minor volcanic constructs can be interpreted from morphological features observed in images. Radar responses that are governed by small-scale surface roughness may serve to distinguish flow types, but do not provide unambiguous information. Imaging of sand dunes is rigorously constrained by specific angular relations between the illumination vector and the orientation and angle of repose of the dune faces, but is independent of radar wavelength. With a single look angle, conditions that enable shallow subsurface imaging to occur do not provide the information necessary to determine whether the radar has recorded surface or subsurface features. The topographic linearity of many tectonic landforms is enhanced on images at regional and local scales, but the detection of structural detail is a strong function of illumination direction. Nontopographic tectonic lineaments may appear in response to contrasts in small-surface roughness or dielectric constant. The breakpoint for rough surfaces will vary by about 25 percent through the Magellan viewing geometries from low to high Venusian latitudes. Examples of anomalies and system artifacts that can affect image interpretation are described
NESTA: A Fast and Accurate First-order Method for Sparse Recovery
Accurate signal recovery or image reconstruction from indirect and possibly
undersampled data is a topic of considerable interest; for example, the
literature in the recent field of compressed sensing is already quite immense.
Inspired by recent breakthroughs in the development of novel first-order
methods in convex optimization, most notably Nesterov's smoothing technique,
this paper introduces a fast and accurate algorithm for solving common recovery
problems in signal processing. In the spirit of Nesterov's work, one of the key
ideas of this algorithm is a subtle averaging of sequences of iterates, which
has been shown to improve the convergence properties of standard
gradient-descent algorithms. This paper demonstrates that this approach is
ideally suited for solving large-scale compressed sensing reconstruction
problems as 1) it is computationally efficient, 2) it is accurate and returns
solutions with several correct digits, 3) it is flexible and amenable to many
kinds of reconstruction problems, and 4) it is robust in the sense that its
excellent performance across a wide range of problems does not depend on the
fine tuning of several parameters. Comprehensive numerical experiments on
realistic signals exhibiting a large dynamic range show that this algorithm
compares favorably with recently proposed state-of-the-art methods. We also
apply the algorithm to solve other problems for which there are fewer
alternatives, such as total-variation minimization, and convex programs seeking
to minimize the l1 norm of Wx under constraints, in which W is not diagonal
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