SAR speckle filtering with CNN using simulated training data

Horst Hammer; Silvia Kuny; Antje Thiele

doi:10.1117/12.3030902

18 November 2024 SAR speckle filtering with CNN using simulated training data

Horst Hammer, Silvia Kuny, Antje Thiele

Proceedings Volume 13195, Microwave Remote Sensing: Data Processing and Applications III; 1319505 (2024) https://doi.org/10.1117/12.3030902
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Abstract

SAR sensors play an important role for a wide range of remote sensing applications due to their all-weather capabilities and their independence from sunlight. However, SAR images are more difficult to interpret than electro-optical images, and are affected by speckle noise, a multiplicative phenomenon that is created by the coherent nature of the image formation process and that might severely hinder image interpretation. Thus, speckle filtering is of great importance and many different approaches have been suggested. While most classical speckle filters use the local statistics in a sliding window, more recently non-local filtering and Convolutional Neural Networks (CNNs) have been used. The approach suggested in this paper falls into the latter category. For supervised CNN approaches, many training chips are necessary to teach the network the task it should solve. In the case of speckle filtering, many speckled/unspeckled image pairs are necessary. In this paper, we suggest creating training data for a U-Net architecture using SAR simulation. The simulation results of several large 3d scenes are shown, which in turn are used for the training of the U-Net for speckle filtering. First results on a TerraSAR-X image are shown and the potential and limitations of the current state of the approach are discussed.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Horst Hammer, Silvia Kuny, and Antje Thiele "SAR speckle filtering with CNN using simulated training data", Proc. SPIE 13195, Microwave Remote Sensing: Data Processing and Applications III, 1319505 (18 November 2024); https://doi.org/10.1117/12.3030902

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