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Multiple feature-enhanced SAR imaging using sparsity in combined dictionaries

Samadi, Sadegh and Çetin, Müjdat and Masnadi-Shirazi, Mohammad Ali (2013) Multiple feature-enhanced SAR imaging using sparsity in combined dictionaries. IEEE Geoscience and Remote Sensing Letters, 10 (4). pp. 821-825. ISSN 1545-598X

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Official URL: http://dx.doi.org/10.1109/LGRS.2012.2225016

Abstract

Nonquadratic regularization-based image formation is a recently proposed framework for feature-enhanced radar imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature, such as strong point scatterers, or smooth regions. However, many scenes contain a number of such feature types. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse representation based on combined dictionaries. This method is developed based on the sparse representation of the magnitude of the scattered complex-valued field, composed of appropriate dictionaries associated with different types of features. The multiple feature-enhanced reconstructed image is then obtained through a joint optimization problem over the combined representation of the magnitude and the phase of the underlying field reflectivities.

Item Type:Article
Uncontrolled Keywords:Complex-valued imaging, image reconstruction, sparse signal representation, synthetic aperture radar (SAR)
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
ID Code:23462
Deposited By:Müjdat Çetin
Deposited On:14 Jan 2014 10:19
Last Modified:14 Jan 2014 10:19

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