A pattern-driven design regularization methodology

2014 ◽  
Author(s):  
Jason P. Cain ◽  
Norma P. Rodriguez ◽  
Jason Sweis ◽  
Frank E. Gennari ◽  
Ya-Chieh Lai
Keyword(s):  
Design Regularization

scholarly journals RASF and DEDR-POCS Image Fusion through Multi-layer Perceptron in SAR Imagery Systems

2019 ◽  
pp. 20-26
Author(s):  
Israel Yañez-Vargas ◽  
Joel Quintanilla-Domínguez ◽  
Gabriel Aguilera-Gonzalez
Keyword(s):  
Synthetic Aperture Radar ◽  
Image Fusion ◽  
Low Cost ◽  
Synthetic Aperture ◽  
Discrete Wavelet ◽  
Multi Layer Perceptron ◽  
Robust Adaptive ◽  
Sar Imagery ◽  
Design Regularization ◽  
Aperture Radar

This paper presents a novel multi-layer perceptron (MLP) based image fusion technique, which fuses two synthetic aperture radar (SAR) images, obtained from the same spatial reflectivity map, acquired with a conventional low-cost fractional synthetic aperture radar (Fr-SAR) system, enhanced via two different methodologies. The first image is enhanced using the traditional descriptive experiment design regularization (DEDR) framework through the projection onto convex solution sets (POCS) method; the second image is enhanced with the DEDR framework by incorporating the robust adaptive spatial filtering (RASF) solution operator. This work describes a MLP based technique applied to the pixel level multi-focus fusion problem characterized by the use of image windows with the idea of reducing noise and determining which pixel is clearer between the two images. Experimental results show that the proposed novel method outperforms the discrete wavelet transform based most competing approach.

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

2018 ◽  
Vol 10 (11) ◽  
pp. 1822 ◽  
Author(s):  
Gustavo Martín del Campo ◽  
Yuriy Shkvarko ◽  
Andreas Reigber ◽  
Matteo Nannini
Keyword(s):  
Structural Model ◽  
Imaging Techniques ◽  
Resolution Enhancement ◽  
Adaptive Beamforming ◽  
Irregular Sampling ◽  
Multi Stage ◽  
Forested Areas ◽  
German Aerospace ◽  
Design Regularization ◽  
Non Parametric

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.

Structured descriptive experiment design regularization based enhancement of fractional SAR imagery

2013 ◽  
Vol 93 (12) ◽  
pp. 3553-3566 ◽  
Author(s):  
Yuriy V. Shkvarko ◽  
José Tuxpan ◽  
Stewart R. Santos
Keyword(s):  
Experiment Design ◽  
Sar Imagery ◽  
Design Regularization
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