A speckle filter for SAR Sentinel-1 GRD data based on Residual
Convolutional Neural Networks
Abstract
In recent years, Machine Learning (ML) algorithms have become widespread
in all the fields of Remote Sensing (RS) and Earth Observation (EO).
This has allowed the rapid development of new procedures to solve
problems affecting these sectors. In this context, this work aims at
presenting a novel method for filtering speckle noise from Sentinel-1
Ground Range Detected (GRD) data by applying Deep Learning (DL)
algorithms, based on Convolutional Neural Networks (CNNs). The paper
provides an easy yet very effective approach to extract the large amount
of training data needed for DL approaches in this challenging case. The
experimental results on simulated speckled images and an actual SAR
dataset show a clear improvement with respect to the state of the art in
terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index
(SSIM), Equivalent Number of Looks (ENL), proving the effectiveness of
the proposed architecture.