NDLI: Multi-signal compressed sensing for polarimetric SAR tomography

Content Provider	IEEE Xplore Digital Library
Author	Aguilera, E. Nannini, M. Reigber, A.
Copyright Year	2011
Description	Author affiliation: Microwaves and Radar Institute, German Aerospace Center (DLR), P.O. Box 1116, 82230 Wessling, Germany (Aguilera, E.; Nannini, M.; Reigber, A.)
Abstract	In recent years, three-dimensional imaging by means of SAR tomography has become a field of intensive research. In SAR tomography, the vertical reflectivity function for every azimuth-range pixel is usually recovered by processing data collected using a defined repeat pass acquisition geometry. The most common approach is to generate a synthetic aperture in the elevation direction through imaging from a large number of parallel tracks. This imaging technique is appealing, since it is very simple. However, it has the drawback that large temporal baselines, which is the case for space-borne platforms, can severely affect the reconstruction. In an attempt to reduce the number of parallel tracks, we propose a new tomographic focusing approach that trades number of SAR images for correlations between neighboring azimuth-range pixels and polarimetric channels. As a matter of fact, this can be done under the framework of Distributed Compressed Sensing (DCS), which stems from Compressed Sensing (CS) theory, thus also exploiting sparsity in our tomographic signal. In addition, we address the problem of measurements affected by additive as well as multiplicative speckle noise. Results demonstrating the potential of the DCS methodology will be validated by using fully polarimetric L-band data acquired by the E-SAR sensor of DLR.
Starting Page	1369
Ending Page	1372
File Size	240944
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049320
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Compressed sensing Tomography Noise reduction Noise Azimuth Sensors Minimization distributed compressed sensing. SAR tomography polarimetry compressed sensing
Content Type	Text
Resource Type	Article

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