期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 5, 期 4, 页码 1272-1281出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2012.2189555
关键词
Compressed sensing (CS); measurement matrix; multi-dictionary; sparse representation; super-resolution (SR); synthetic aperture radar (SAR)
类别
资金
- NSFC [60702041, 41174120]
- China Postdoctoral Science Foundation
- LIESMARS Special Research Funding
This paper presents a novel approach for the reconstruction of super-resolution (SR) synthetic aperture radar (SAR) images in the compressed sensing (CS) theory framework. Recent research has shown that super-resolved data can be reconstructed from an extremely small set of measurements compared to that currently required. Therefore, a CS to produce SAR super-resolution images is introduced in the present work. The proposed approach contributes in three ways. First, enhanced SR results are achieved using a framework that combines CS with a multi-dictionary. Then, the multi-dictionary pairs are trained after classifying the training images through a sparse coding spatial pyramid machine. Each dictionary pair containing low-and high-resolution dictionaries are jointly trained. Finally, the gradient-descent optimization approach is applied to decrease the mutual coherence between the measurement matrix and the representation basis. The CS reconstruction effect is related to incoherence. The effectiveness of this method is demonstrated on TerraSAR-X data.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据