Multiple Feature-Enhanced SAR Imaging Using Sparsity in Combined Dictionaries
Sadegh Samadi, Müjdat Çetin, Mohammad Ali Masnadi-Shirazi
IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 4, pp. 821-825, July 2013
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 themagnitudeof the scattered complex-valued field, composed of appropriate dictionaries as-sociated 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.