
Proceedings Paper
Reconstruction of interrupted SAR imagery for persistent surveillance change detectionFormat | Member Price | Non-Member Price |
---|---|---|
$17.00 | $21.00 |
Paper Abstract
In this paper we apply a sparse signal recovery technique for synthetic aperture radar (SAR) image formation
from interrupted phase history data. Timeline constraints imposed on multi-function modern radars result in
interrupted SAR data collection, which in turn leads to corrupted imagery that degrades reliable change detection.
In this paper we extrapolate the missing data by applying the basis pursuit denoising algorithm (BPDN) in the
image formation step, effectively, modeling the SAR scene as sparse. We investigate the effects of regular and
random interruptions on the SAR point spread function (PSF), as well as on the quality of both coherent (CCD)
and non-coherent (NCCD) change detection. We contrast the sparse reconstruction to the matched filter (MF)
method, implemented via Fourier processing with missing data set to zero. To illustrate the capabilities of the
gap-filling sparse reconstruction algorithm, we evaluate change detection performance using a pair of images
from the GOTCHA data set.
Paper Details
Date Published: 2 May 2012
PDF: 16 pages
Proc. SPIE 8394, Algorithms for Synthetic Aperture Radar Imagery XIX, 839408 (2 May 2012); doi: 10.1117/12.925069
Published in SPIE Proceedings Vol. 8394:
Algorithms for Synthetic Aperture Radar Imagery XIX
Edmund G. Zelnio; Frederick D. Garber, Editor(s)
PDF: 16 pages
Proc. SPIE 8394, Algorithms for Synthetic Aperture Radar Imagery XIX, 839408 (2 May 2012); doi: 10.1117/12.925069
Show Author Affiliations
Les Novak, Scientific Systems Co. (United States)
Published in SPIE Proceedings Vol. 8394:
Algorithms for Synthetic Aperture Radar Imagery XIX
Edmund G. Zelnio; Frederick D. Garber, Editor(s)
© SPIE. Terms of Use
