High-Definition Vector Imaging
نویسنده
چکیده
■ High-definition vector imaging (HDVI) is a data-adaptive approach to synthetic-aperture radar (SAR) image reconstruction based on superresolution techniques originally developed for passive sensor arrays. The goals are to produce more informative, higher-resolution imagery for improving target recognition with UHF and millimeter-wave SAR and to aid the image analyst in identifying targets in radar imagery. Algorithms presented here include twodimensional minimum-variance techniques based on the maximum-likelihood method (Capon) algorithm and a two-dimensional version of the MUSIC algorithm. We use simulations to compare processing techniques, and we present results of wideband Rail SAR measurements of reflectors in foliage, demonstrating resolution improvement and clutter rejection. Results with airborne millimeter-wave SAR data demonstrate improved resolution and speckle reduction. We also discuss the vector aspect of HDVI, i.e., the incorporation of non-pointlike scattering models to enable feature detection. An example of a vector image is presented for data from an airborne UHF radar, using the broadside flash model to reveal greater information in the data.
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