Abstract:
Sparse signal processing has been applied in synthetic-aperture radar (SAR) imaging. As a typical sparse reconstruction model, L1 regularization often underestimates the ...Show MoreMetadata
Abstract:
Sparse signal processing has been applied in synthetic-aperture radar (SAR) imaging. As a typical sparse reconstruction model, L1 regularization often underestimates the intensities of the targets. The estimated radar cross section (RCS) is related to the pixel intensity. Thus, the linear relationship between the targets' intensities cannot kept. The underestimation will also cause radiometric errors and affect the quantitative use of the SAR data. In this letter, we present a SAR imaging method based on generalized minimax concave (GMC) penalty. GMC is a nonconvex penalty and its cost function is convex. GMC can avoid the underestimation of pixel intensity. In the iteration, the azimuth-range decouple operators are used to avoid the huge memory and computational costs. The performance of the proposed method is verified using real data.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 16, Issue: 10, October 2019)