Research Article
Sparse Representation Based SAR Imaging Using Combined Dictionary
@INPROCEEDINGS{10.1007/978-3-319-73447-7_15, author={Han-yang Xu and Feng Zhou}, title={Sparse Representation Based SAR Imaging Using Combined Dictionary}, 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 Sparse representation Dictionary learning Combined dictionary}, doi={10.1007/978-3-319-73447-7_15} }- Han-yang Xu
Feng Zhou
Year: 2018
Sparse Representation Based SAR Imaging Using Combined Dictionary
MLICOM
Springer
DOI: 10.1007/978-3-319-73447-7_15
Abstract
Sparse representation (SR)-based SAR imaging has shown its superior capability in high-resolution image formation. For SR-based SAR imaging task, a key challenge is how to choose a proper dictionary that can effectively represent the magnitude of the complex-valued scattering field. In this paper, we present a combined dictionary that simultaneously enhances multiple types of scattering mechanism. Trained by different kinds of SAR image patches with either strong point scatterers or smooth regions, the dictionary can represent both point-scattering and spatially distributed scenes sparsely. Finally, the SAR image is obtained by solving a joint optimization problem over the combined representation of the magnitude and phase of the observed scene.
