Polarimetric SAR Despeckling With Convolutional Neural Networks

• Published in: IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 12
• Main Authors: Tucker, David, Potter, Lee C.
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additives; Artificial neural networks; Convolutional neural networks (CNNs); Covariance matrices; Covariance matrix; Deep learning; Neural networks; Polarimetry; Radar imaging; Radar polarimetry; SAR (radar); Scattering; Speckle; Synthetic aperture radar; synthetic aperture radar (SAR); Task analysis
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2022.3152068

Coherent imaging systems such as synthetic aperture radar (SAR) are subject to speckle, the reduction of which is an active area of study. Methods based on deep convolutional neural networks (CNNs) have recently demonstrated state-of-the-art performance in the removal of additive noise from natural images and speckle from single-channel SAR images. The application of deep learning to multichannel SAR modalities such as polarimetric SAR (PolSAR) is complicated in part by the nature of the data as images of complex-valued covariance matrices. In this article, we propose a CNN-based PolSAR despeckling approach that uses an invertible transformation involving a matrix logarithm to facilitate CNN processing of the PolSAR data. A residual learning strategy is adopted, in which the CNN is trained to identify the speckle component which is then removed from the corrupted image. The experimental results on simulated and measured PolSAR data show the proposed approach to markedly reduce speckle and preserve scene features.