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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Shkvarko, Y. V. Amao, J. A. Yanez, J. I. IPN CINVESTAV Unidad Guadalajara Mexico City DF Mexico

ISBN: (纸本)9781479979295

The challenge of this study is to develop a new approach for multi-stage feature enhanced recovery of remote sensing (RS) imagery. The approach is based on modeling the spatial spectrum pattern (SSP) reflectivity map as a superposition of different image structures, i.e., edges, smooth and homogeneous texture zones. The latter usually manifest sparsity properties in some specific component dictionaries. The innovative proposition relates to incorporating into the initial descriptive experiment design regularization (DEDR) framework two additional regularization modalities: (i) the compressive sensing (CS) inspired convergence guaranteed regularizing projections onto convex solution sets (POCS) and (ii) the adaptive sparsity preserving despeckling level that performs the dictionary-based restoration (DBR) of the image features represented in the employed Haar wavelet dictionary basis. Algorithmically, the DBR processing is implemented as the shrinkage-type iterative CS technique adaptively incorporated into the overall multi-stage iterative DEDRDBR method.

关键词： descriptive experiment design dictionary regularization restoration

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Unified descriptive experiment design regularization and component dictionary-based image restoration approach for enhanced radar/SAR sensing

Unified descriptive experiment design regularization and com...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Y. V. Shkvarko J. A. Amao J. I. Yañez CINVESTAV del IPN Unidad Guadalajara México

ISBN: (数字)9781479979295

ISBN: (纸本)9781479979301

关键词： descriptive experiment design Dictionary Regularization Restoration

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descriptive experiment design UNIFIED WITH WORST-CASE PERFORMANCE OPTIMIZATION-ADAPTED REGULARIZATION FOR HIGH-RESOLUTION RADAR/SAR IMAGING

DESCRIPTIVE EXPERIMENT DESIGN UNIFIED WITH WORST-CASE PERFOR...

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2008 IEEE Radar Conference

作者： Shkvarko, Yuriy V. CINVESTAV IPN Guadalajara 45010 Jal Mexico

ISBN: (纸本)9781424415380

This paper considers the problem of high-resolution imaging of the environment formalized in terms Of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the wavefield scattered from an extended remotely sensed scene (referred to as the scene image) via processing the discrete measurements of a finite number of independent realizations of the observed degraded radar data signals (one realization of the trajectory signal in the case of SAR). The model-level uncertainties are associated with unknown statistics of perturbations of the signal formation operator (SFO) in turbulent environment. The system-level uncertainties are attributed to the imperfect array calibration, finite dimensionality of measurements, uncontrolled antenna vibrations and random carrier trajectory deviations in the case of SAR. An effective method for SSP reconstruction is therefore proposed by incorporating into the minimum risk (AM) nonparametric spectral estimation strategy the experiment design-motivated constraints of SSP observability/identifiability for the finite-dimensional range continuous-to-discrete SFO algorithmically coupled with descriptive experiment design regularization (DEDR) and unified with worst-case statistical performance optimization approach. The MR objective functional is constrained by this information, and the robust DEDR reconstruction operator applicable to the scenarios with the low-rank uncertain estimated data correlation matrices is found We also show how this algorithm may be considered generalization of the robust MVDR and the regularized inverse spatial filtering techniques. The efficiency of the developed technique is illustrated via numerical simulations.

关键词： descriptive experiment design regularization

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Unifying experiment design and Convex Regularization Techniques for Enhanced Imaging With Uncertain Remote Sensing Data-Part II: Adaptive Implementation and Performance Issues

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2010年第1期48卷 96-111页

作者： Shkvarko, Yuriy V. Inst Politecn Nacl Ctr Invest & Estudios Avanzados Dept Elect Engn Guadalajara 45015 Jalisco Mexico

The unified descriptive experiment design regularization (DEDR) method from a companion paper provides a rigorous theoretical formalism for robust estimation of the power spatial spectrum pattern of the wavefield scattered from an extended scene observed in the uncertain remote sensing (RS) environment. For the considered here imaging synthetic aperture radar (SAR) application, the proposed DEDR approach is aimed at performing, in a single optimized processing, SAR focusing, speckle reduction, and RS scene image enhancement and accounts for the possible presence of uncertain trajectory deviations. Being nonlinear and solution dependent, the optimal DEDR estimator requires rather complex signal processing operations ruled by the fixed-point iterative implementation process. To simplify further the processing, in this paper, we propose to incorporate the descriptive regularization via constructing the projections onto convex sets that enable us to factorize and parallelize the reconstructive image processing over the range and azimuth coordinates, design a family of such regularized easy-to-implement iterative algorithms, and provide the relevant computational recipes for their application to fractional imaging SAR. We also comment on the adaptive adjustment of the DEDR operational parameters directly from the actual speckle-corrupted scene images and demonstrate the effectiveness of the proposed adaptive DEDR techniques.

关键词： Convex solution sets descriptive experiment design despeckling regularization spatial spectrum pattern (SSP) synthetic aperture radar (SAR)

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