SAR image despeckling using deep CNN

Abstract

Synthetic aperture radar (SAR) images are contaminated with noise called speckle that is multiplicative in nature. The presence of speckles in SAR images makes it impossible to understand and interpret for extensive range of applications. However, certain characteristics of SAR and the ability to function irrespective of weather conditions, are making such images worth processing in order to be able to extract relevant information. A despeckling model is proposed that uses deeper convolutional neural networks, which was never used before, as far as authors are concerned, for diminishing speckle in noisy SAR images. Multiple skip connections from the ResNet model are also employed in authors' proposed architecture. In order to maintain uniformity, a formula to be followed while applying skip connections is also derived. A hybrid loss function is developed to train the network more consistently to achieve the desired output. Experiments on simulated SAR images using the NWPU-RESISC benchmark are conducted and tested on real TerraSAR-X images and compared with the state-of-the-art techniques. Results show that the proposed method achieved considerable improvements compared to state-of-the-art methods with PSNRs 27.02, 24.60, and 22.01 for looks 10, 4, and 1, respectively.