Regularized resolution enhancement of point-based features of synthetic aperture radar image using variable quasi-norm
Nonquadratic regularization with lp quasi-norm has been widely used as an efficient and powerful tool for resolution enhancement of point-based features of synthetic aperture radar (SAR) images. However, adjustment of the lp quasi-norm usually requires a lot of time and labor. In this paper, we propose a modified model and method for choosing regularization term. Considering the sparseness of scatterers in the scene of a SAR image, we use the generalized Gaussian distributions (GGD) as prior distributions for sampled scattering field. Our regularization model is constructed based on variable lp quasi-norm s, and the selection of lp quasi-norm is achieved through estimation of the shape parameter p of the GGD by adopting a moment method. The regularization model leads to an alternating iterative algorithm. Experimental results with simulated and real data show that the method can automatically select regularization term and produce SAR images with improved spatial resolution.
SAR resolution enhancement variable quasinorm regularizaiion parameter estimation point-based feature
Guangxin Wang Hong Luo Zhenrong Zhang
Guilin Air Force Academy Guilin, China School of Computer, Electronics and Information,Guangxi University,Nanning, China
国际会议
2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)
大连
英文
500-504
2010-07-05（万方平台首次上网日期，不代表论文的发表时间）