Deep Learning-Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1

Speckle suppression in synthetic aperture radar (SAR) images is a key processing step which continues to be a research topic. A wide variety of methods, using either spatially-based approaches or transform-based strategies, have been developed and have shown to provide outstanding results. However, recent advances in deep learning techniques and their application to SAR image despeckling have been demonstrated to offer state-of-the-art results. Unfortunately, they have been mostly applied to single-polarimetric images. The extension of a deep learning-based approach for speckle removal to polarimetric SAR (PolSAR) images is complicated because of the complex nature of the covariance matrices measured for every image pixel, the properties of which must be preserved during filtering. In this work, we propose a complete framework to remove speckle in PolSAR images using a convolutional neural network (CNN). The methodology includes a reversible transformation of the original complex covariance matrix to obtain a set of real-valued intensity bands which are fed to the neural network. In addition, the proposed method includes a change detection strategy to avoid the neural network to learn erroneous features in areas affected by temporal changes, so that the network only learns the underlying speckle component present in the data. The method is implemented and tested with dual-polarimetric images acquired by Sentinel-1. The different experiments carried out in this work show that the proposed methodology provides good results in both speckle reduction and resolution preservation, similar to or even better than the ones obtained with state-of-the-art methods. Also importantly, it is shown that the neural network is not generating artifacts or introducing bias in the filtered images, making them suitable for further polarimetric processing and exploitation.