Optimal-Dual-Based $l_1$ Analysis for Speckle Reduction of SAR Data
Dušan Gleich · IEEE Transactions on Geoscience and Remote Sensing · 2018
Synthetic aperture radar (SAR) images are affected by a speckle noise, which is a consequence of random fluctuations in the return signal from an object that is no bigger than a single image processing element and it is caused by coherent processing of backscattered signals from multiple distributed targets. Speckle within SAR images can be reduced using filtering methods. To preserve features within the SAR images, this paper proposes a noise removal based on scene and SAR data modeling. The proposed method is a model-based total variational optimization with the minimization of a cost function. The cost function consisted of energy and data fidelity terms. The energy term was modeled using optimal-dual-based l1analysis. The data fidelity term modeled the amplitude of the SAR data, which was approximated using a Nakagami distribution. The minimization of the cost function was solved using a quasi-Newton approach. The experimental results showed good results in SAR feature preservation. The proposed method was evaluated numerically using quality metrics for synthetic generated data and real amplitude SAR data.