Feature Enhanced Imaging with Compressed Fractional SAR Sensors: Inverse Problem Formalism and l2–l1 Structured Descriptive Regularization Framework

Konferenz: EUSAR 2016 - 11th European Conference on Synthetic Aperture Radar
06.06.2016 - 09.06.2016 in Hamburg, Germany

Tagungsband: EUSAR 2016

Seiten: 4Sprache: EnglischTyp: PDF

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Shkvarko, Yuriy (Cinvestav-IPN, Unidad Guadalajara, Mexico)
Reigber, Andreas (Microwaves and Radar Institute, German Aerospace Center, Germany)
Garcia, Guillermo (CUCEI-UdeG, Mexico)

We address a new technique for feature-enhanced radar imaging with compressed/fractional SAR data that unifies the descriptive experiment design regularization (DEDR) framework with the total variation (TV) image enhancement paradigm and the sparsity preserving regularizing projections onto convex solution sets (POCS). The new framework incorporates the L1 metric structured TV regularization into the L2 metric structured DEDR data agreement objective function and solves the overall reconstructive imaging inverse problem employing the POCD-DEDR-TV-restructured MVDR strategy.