Chen, J., Wang, K., Yang, W. et al. (1 more author) (2017) Accurate Reconstruction and Suppression for Azimuth Ambiguities in Spaceborne Stripmap SAR Images. IEEE Geoscience and Remote Sensing Letters, 14 (1). pp. 102-106. ISSN 1545-598X
Abstract
In this letter, an accurate mathematical model for azimuth ambiguity in stripmap synthetic aperture radar (SAR) images is first constructed, with an azimuth ambiguity factor (AAF) defined as the residual amplitude and phase terms of ambiguities. Next, a novel framework for reconstructing and suppressing azimuth ambiguity is proposed based on the analysis of the AAF. In this framework, azimuth ambiguities are accurately reconstructed by applying reconstruction filters in the range Doppler and 2-D frequency domain, and then, the reconstructed signal is used for suppressing azimuth ambiguities. Moreover, the proposed framework does not depend on the statistical characteristics of a SAR image and is capable of reducing the space-variant ambiguities. As verified by both simulated data and real TerraSAR-X data, the proposed method is capable of suppressing azimuth ambiguities in SAR images.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Ambiguity reconstruction; ambiguity suppression; azimuth ambiguities; synthetic aperture radar (SAR) |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Mar 2017 10:23 |
Last Modified: | 21 Mar 2018 00:55 |
Published Version: | https://doi.org/10.1109/LGRS.2016.2630122 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/LGRS.2016.2630122 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:113282 |