Modeling and simulation of the fused Bayesian-regularization method for remote sensing imagery with synthetic aperture arrays

A new fused Bayesian regularization (FBR) method for enhanced remote sensing imaging based on a new concept of aggregated statistical-deterministic regularization was developed recently. In this study, we represent the results of modeling and extensive simulation of the FBR algorithms for enhanced reconstruction of the spatial spectrum patterns (SSP) of the point-type and spatially distributed wavefield sources as it is required for the remote sensing imagery with synthetic aperture arrays. The simulations were performed in the MATLAB computational environment for the family of the SAR imaging algorithms that employed different modifications of the FBR method. The presented results enable one to evaluate the operational performance of the FBR method that was not previously reported in the literature

New spectral positional invariance approach for superresolution of point-type targets embedded in colored noise

A new technique is proposed for high-resolution spectral estimation of point-type targets in multi-grade background scenes. All existing spectral estimation methods operating with short data lengths face the contradiction of providing superresolution of the spectral components related to distinct signal sources, and the smoothed reconstruction of the spectral shape of the extended component as a whole. The method addressed here implies considering the spectrum estimation problem under two paradigms: (i) the Prony estimation of the point-type targets; (ii) the nonparametric maximum entropy estimation applied to reconstruct the image of the background scene. By fusing these two methods, we achieve a substantial improvement in resolutions of point-type targets as well as the background spectral characterization

TomoSAR Imaging for the Study of Forested Areas: A Virtual Adaptive Beamforming Approach

Among the objectives of the upcoming space missions Tandem-L and BIOMASS, is the 3-D representation of the global forest structure via synthetic aperture radar (SAR) tomography (TomoSAR). To achieve such a goal, modern approaches suggest solving the TomoSAR inverse problems by exploiting polarimetric diversity and structural model properties of the different scattering mechanisms. This way, the related tomographic imaging problems are treated in descriptive regularization settings, applying modern non-parametric spatial spectral analysis (SSA) techniques. Nonetheless, the achievable resolution of the commonly performed SSA-based estimators highly depends on the span of the tomographic aperture; furthermore, irregular sampling and non-uniform constellations sacrifice the attainable resolution, introduce artifacts and increase ambiguity. Overcoming these drawbacks, in this paper, we address a new multi-stage iterative technique for feature-enhanced TomoSAR imaging that aggregates the virtual adaptive beamforming (VAB)-based SSA approach, with the wavelet domain thresholding (WDT) regularization framework, which we refer to as WAVAB (WDT-refined VAB). First, high resolution imagery is recovered applying the descriptive experiment design regularization (DEDR)-inspired reconstructive processing. Next, the additional resolution enhancement with suppression of artifacts is performed, via the WDT-based sparsity promoting refinement in the wavelet transform (WT) domain. Additionally, incorporation of the sum of Kronecker products (SKP) decomposition technique at the pre-processing stage, improves ground and canopy separation and allows for the utilization of different better adapted TomoSAR imaging techniques, on the ground and canopy structural components, separately. The feature enhancing capabilities of the novel robust WAVAB TomoSAR imaging technique are corroborated through the processing of airborne data of the German Aerospace Center (DLR), providing detailed volume height profiles reconstruction, as an alternative to the competing non-parametric SSA-based methods

Antenna-based processing of the radar data for zone detection in remote sensing imagery

A new approach is addressed to perform antenna-based processing of radar imagery data aimed at reconstruction/enhancement of the images degraded by a spatially-shift-invariant radar spread function and contaminated with additive Gaussian noise. The fused maximum entropy-variational method is developed and computationally implemented using the modified Hopfield neural network. Enhanced zone detection and image denoising are achieved using the proposed approach