Research Article
A Novel Parameter Determination Method for Lq Regularization Based Sparse SAR Imaging
@INPROCEEDINGS{10.1007/978-3-319-73447-7_18, author={Jia-cheng Ni and Qun Zhang and Li Sun and Xian-jiao Liang}, title={A Novel Parameter Determination Method for Lq Regularization Based Sparse SAR Imaging}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part II}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={SAR imaging Lq(0 < q < 1) regularization Regularization parameter determination}, doi={10.1007/978-3-319-73447-7_18} }- Jia-cheng Ni
Qun Zhang
Li Sun
Xian-jiao Liang
Year: 2018
A Novel Parameter Determination Method for Lq Regularization Based Sparse SAR Imaging
MLICOM
Springer
DOI: 10.1007/978-3-319-73447-7_18
Abstract
Sparse SAR imaging based on Lq(0 < q < 1) regularization has become a hot issue in SAR imaging. However, it can be difficult to determine a suitable value of the regularization parameter. In this paper, we developed a novel adaptive regularization parameter determination method for Lq regularization based SAR imaging. On the basis that the noise type in SAR system is mostly additive Gaussian white noise, we present a method for determining the regularization parameter through evaluating the statistics of noise. The parameter is updated through validating the statistical properties of the reconstruction error residuals in a suitable Noise Confidence Region (NCR). The experiment results illustrate the validity of the proposed method.