SAR Despeckling Using Overcomplete Convolutional Networks
This addresses speckle degradation in SAR images for remote sensing applications, but it is incremental as it builds on existing CNN approaches with a specific architectural modification.
The paper tackles SAR image despeckling by proposing an overcomplete CNN architecture that restricts the receptive field to better learn low-level speckle features, showing improved performance compared to recent methods on synthetic and real SAR images.
Synthetic Aperture Radar (SAR) despeckling is an important problem in remote sensing as speckle degrades SAR images, affecting downstream tasks like detection and segmentation. Recent studies show that convolutional neural networks(CNNs) outperform classical despeckling methods. Traditional CNNs try to increase the receptive field size as the network goes deeper, thus extracting global features. However,speckle is relatively small, and increasing receptive field does not help in extracting speckle features. This study employs an overcomplete CNN architecture to focus on learning low-level features by restricting the receptive field. The proposed network consists of an overcomplete branch to focus on the local structures and an undercomplete branch that focuses on the global structures. We show that the proposed network improves despeckling performance compared to recent despeckling methods on synthetic and real SAR images.