Just Project! Multi-Channel Despeckling, the Easy Way
Lo\"ic Denis (LabHC, IMAGES, IDS), Emanuele Dalsasso (EPFL, IMAGES), Florence Tupin (IMAGES, IDS)

TL;DR
This paper presents MuChaPro, a versatile framework for multi-channel SAR despeckling that leverages existing single-channel methods and self-supervised learning to improve performance in polarimetric and interferometric applications.
Contribution
MuChaPro introduces a simple, effective approach to multi-channel SAR despeckling by generating and restoring single-channel projections, enabling sensor-specific self-supervised training.
Findings
Effective in polarimetric and interferometric SAR imaging
Compatible with existing single-channel despeckling methods
Enables sensor-specific self-supervised learning
Abstract
Reducing speckle fluctuations in multi-channel SAR images is essential in many applications of SAR imaging such as polarimetric classification or interferometric height estimation. While single-channel despeckling has widely benefited from the application of deep learning techniques, extensions to multi-channel SAR images are much more challenging. This paper introduces MuChaPro, a generic framework that exploits existing single-channel despeckling methods. The key idea is to generate numerous single-channel projections, restore these projections, and recombine them into the final multi-channel estimate. This simple approach is shown to be effective in polarimetric and/or interferometric modalities. A special appeal of MuChaPro is the possibility to apply a self-supervised training strategy to learn sensor-specific networks for single-channel despeckling.
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Taxonomy
TopicsBIM and Construction Integration · Reservoir Engineering and Simulation Methods · Software Engineering Research
