Synthetic aperture radar image despeckling neural network based on maximum a posteriori probability estimation

Abstract

ABSTRACT Automatic despeckling from synthetic aperture radar (SAR) imagery plays a significant role in many urban applications. Recently, owing to the impressive performance of deep learning, various SAR despeckling methods based on the convolutional neural network (CNN) have been proposed for optical SAR images. However, existing CNN-based despeckling methods and spatial-domain-based methods exhibit certain limitations. The introduction of prior knowledge is not considered in the designing of classical denoising and certain CNN-based methods, thus leading to the loss of edge information and artefacts in the results. To address this issue, an a priori-based deep neural network SAR image despeckling method (SAR-PBDNN) was proposed. In this approach, the Bayesian formula is modelled to calculate the objective function suitable for the despeckling problem and is optimized using the alternating direction method of multipliers algorithm and deep learning method. The introduction of prior information effectively improved the denoising effect of SAR images. The SAR-PBDNN was evaluated using both simulated and real SAR images. The comparisons demonstrate the effectiveness of the proposed method. We conclude that the SAR-PBDNN has the potential to automatically despeckle SAR images with an accuracy that renders it a useful tool for practical application scenarios.