SAR Despeckling Using Overcomplete Convolutional Networks

Abstract

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. Our code is available at : https://github.com/malshaV/sar_overcomplete

Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention Mechanism

“…Therefore, those datasets were also included in the learning process. The proposed SDRCNN and ABCNN methods were compared to the SARBM3D [8], DCNN [20], overcomplete convolutional neural network (OCNN) [35], and SAR image despeckling using a continuous attention module (SAR-CAM) [37] methods.…”

“…Recently, published methods for SAR image despeckling have used convolutional neural networks [32][33][34][35][36]. The authors of [32] proposed a residual network known as SAR-DRDNet, which consists of non-local and detail recovery parts and uses the global information of the SAR image and multiscale contextual information of the pixels.…”

“…The despeckled image is obtained through a reverse process that predicts the added noise iteratively using a noise predictor conditioned on the speckled image. The authors of [35] employed an overcomplete CNN architecture to focus on learning low-level features by restricting the receptive field. The proposed network consists of an overcomplete branch that focuses on the local structures and an undercomplete branch that focuses on the global structures.…”