TY - JOUR
T1 - GB-SAR Interferometry Based on Dimension-Reduced Compressive Sensing and Multiple Measurement Vectors Model
AU - Feng, Weike
AU - Nico, Giovanni
AU - Sato, Motoyuki
N1 - Funding Information:
Manuscript received June 24, 2018; revised August 3, 2018; accepted August 18, 2018. Date of publication September 6, 2018; date of current version December 26, 2018. This work was supported by the Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research under Grant 26249058. (Corresponding author: Weike Feng.) W. Feng is with the Graduate School of Environmental Studies, Tohoku University, Sendai 980-8576, Japan (e-mail: feng.weike.q4@dc.tohoku.ac.jp). G. Nico is with the Istituto per le Applicazioni del Calcolo, Consiglio Nazionale delle Ricerche, 70126 Bari, Italy (e-mail: g.nico@ba.iac.cnr.it). M. Sato is with the Center for Northeast Asian Studies, Tohoku University, Sendai 980-8576, Japan (e-mail: sato@cneas.tohoku.ac.jp). Color versions of one or more of the figures in this letter are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/LGRS.2018.2866600
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2019/1
Y1 - 2019/1
N2 - To reduce the data acquisition time and the high-level sidelobes produced by conventional focusing methods for ground-based synthetic aperture radar interferometry, we present a new method to provide accurate displacement maps based on the dimension-reduced compressive sensing (CS) method combined with the multiple measurement vectors (MMVs) model. The proposed CS method consists in selecting the supported area of targets, estimated by the fast conventional method with undersampled data. The following sparse reconstruction is applied only to the selected areas. The MMV-based approach allows increasing the coherence and the precision of displacement estimates. Two experiments are carried out to assess the performance of the proposed method.
AB - To reduce the data acquisition time and the high-level sidelobes produced by conventional focusing methods for ground-based synthetic aperture radar interferometry, we present a new method to provide accurate displacement maps based on the dimension-reduced compressive sensing (CS) method combined with the multiple measurement vectors (MMVs) model. The proposed CS method consists in selecting the supported area of targets, estimated by the fast conventional method with undersampled data. The following sparse reconstruction is applied only to the selected areas. The MMV-based approach allows increasing the coherence and the precision of displacement estimates. Two experiments are carried out to assess the performance of the proposed method.
KW - Compressive sensing (CS)
KW - SAR
KW - SAR interferometry
KW - ground-based synthetic aperture radar (GB-SAR)
KW - multiple measurement vectors (MMVs) model
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U2 - 10.1109/LGRS.2018.2866600
DO - 10.1109/LGRS.2018.2866600
M3 - Article
AN - SCOPUS:85053149449
VL - 16
SP - 70
EP - 74
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
SN - 1545-598X
IS - 1
M1 - 8456836
ER -