NDLI: Numerical simulation of tomographic-SAR imaging and object reconstruction using compressive sensing with L $

Content Provider	Springer Nature Link
Author	Wang, Xiao Xu, Feng Jin, Ya Qiu
Copyright Year	2014
Abstract	By making use of multiple acquisitions of synthetic aperture radar (SAR) observations over the same area, tomographic-SAR (tomo-SAR) technology can achieve three-dimensional (3-D) imaging of the objects of interest. The compressive sensing (CS) approach has been applied to deal with the sparseness of the elevation signals. Due to its sparsity and convexity, the L $_{1}$-norm regularization, as an approximated L $_{0}$-norm with an exact solution, has been employed in CS to reconstruct the reflectivity profile of the objects. In this paper, based on our studies on polarimetric scattering and SAR imaging simulations, we produce numerical multi-pass tomo-SAR observations of the terrain object. Then, we present the CS with novel L $_{1/2}$-norm regularization to realize 3-D reconstruction. As a non-convex optimization problem, the L $_{1/2}$-norm regularization is solved by an iterative algorithm. This numerical simulation of tomo-SAR imaging and 3-D reconstruction of the object modeling can be of great help for parameterized analysis of tomo-SAR imagery. As an example, a tomo-SAR image and 3-D reconstruction of the Beijing National Stadium model are presented.
Starting Page	4600
Ending Page	4607
Page Count	8
File Format	PDF
ISSN	10016538
Journal	Chinese Science Bulletin
Volume Number	59
Issue Number	33
e-ISSN	18619541
Language	English
Publisher	Science China Press
Publisher Date	2014-07-15
Publisher Place	Heidelberg
Access Restriction	Subscribed
Subject Keyword	Numerical simulation of pol-scattering Tomographic-SAR Compressive sensing L $_{1/2}$-norm regularization 3-D reconstruction Science Life Sciences Physics Chemistry/Food Science Earth Sciences Engineering
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Numerical simulation of tomography-SAR imaging and the object reconstruction using the compressive sensing approach with L1/2-norm regularization

Three-Dimensional Reconstruction of Man-Made Objects Using Polarimetric Tomographic SAR

Tomographic SAR Inversion by $L_{1}$ -Norm Regularization—The Compressive Sensing Approach

Introducing spatial regularization in SAR tomography reconstruction

A novel frequency domain image reconstruction based on the Tikhonov regularization and robust estimation technique for Compressive Sensing

Super-resolution SAR tomography focusing by $l^{p}$ — Norm regularization-the FOCUSS algorithm

A strategy of SAR imaging based on 2-D block compressive sensing

Simple object reconstruction using compressive sampling

A Novel Compressive Sensing Algorithm for SAR Imaging

Numerical simulation of tomographic-SAR imaging and object reconstruction using compressive sensing with L $_{1/2}$-norm regularization

Similar Documents

Numerical simulation of tomography-SAR imaging and the object reconstruction using the compressive sensing approach with L1/2-norm regularization

Three-Dimensional Reconstruction of Man-Made Objects Using Polarimetric Tomographic SAR

Tomographic SAR Inversion by $L_{1}$ -Norm Regularization—The Compressive Sensing Approach

Introducing spatial regularization in SAR tomography reconstruction

A novel frequency domain image reconstruction based on the Tikhonov regularization and robust estimation technique for Compressive Sensing

Super-resolution SAR tomography focusing by $l^{p}$ — Norm regularization-the FOCUSS algorithm

A strategy of SAR imaging based on 2-D block compressive sensing

Simple object reconstruction using compressive sampling

A Novel Compressive Sensing Algorithm for SAR Imaging

Numerical simulation of tomographic-SAR imaging and object reconstruction using compressive sensing with L $_{1/2}$-norm regularization