Just Project! Multi-Channel Despeckling, the Easy Way
This addresses the problem of multi-channel despeckling for SAR imaging applications like polarimetric classification, offering a generic and incremental solution.
The paper tackles the challenge of reducing speckle noise in multi-channel SAR images by introducing MuChaPro, a framework that projects multi-channel data into numerous single-channel projections, applies existing single-channel despeckling methods, and recombines them, showing effectiveness in polarimetric and interferometric modalities with a self-supervised training strategy.
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.