A Novel Compressive Sensing Algorithm for SAR Imaging

A novel compressive sensing (CS) algorithm for synthetic aperture radar (SAR) imaging is proposed which is called as the two-dimensional double CS algorithm (2-D-DCSA). We first derive the imaging operator for SAR, which is named as the chirp-scaling operator (CSO), from the chirp-scaling algorithm...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE journal of selected topics in applied earth observations and remote sensing Ročník 7; číslo 2; s. 708 - 720
Hlavní autori: Dong, Xiao, Zhang, Yunhua
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.02.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Azimuth
Chirp-scaling algorithm (CSA)
compressive sensing (CS)
Image reconstruction
Imaging
orthonormal basis
Radar
Radar polarimetry
Sparse matrices
Synthetic aperture radar
synthetic aperture radar (SAR)
two-dimensional double CS Algorithm (2-D-DCSA)
Vectors
ISSN:1939-1404, 2151-1535
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:A novel compressive sensing (CS) algorithm for synthetic aperture radar (SAR) imaging is proposed which is called as the two-dimensional double CS algorithm (2-D-DCSA). We first derive the imaging operator for SAR, which is named as the chirp-scaling operator (CSO), from the chirp-scaling algorithm (CSA), then we show its inverse is a linear map, which transforms the SAR image to the received baseband radar signal. We show that the SAR image can be reconstructed simultaneously in the range and azimuth directions from a small number of the raw data. The proposed algorithm can handle large-scale data because both the CSO and its inverse allow fast matrix-vector multiplications. Both the simulated and real data are processed to test the algorithm and the results show that the 2-D-DCSA can be applied to reconstructing the SAR images effectively with much less data than regularly required.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2013.2291578