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.
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61471284, 61522114, 61631019 and by the NSAF under Grant U1430123; it was also supported by the Young Scientist Award of Shaanxi Province under Grants 2015KJXX-19 and 2016KJXX-82.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Xu, Hy., Zhou, F. (2018). Sparse Representation Based SAR Imaging Using Combined Dictionary. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-319-73447-7_15
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DOI: https://doi.org/10.1007/978-3-319-73447-7_15
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