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SAR image despeckling through convolutional neural networks

Abstract : In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitem-poral SAR image and its multilook version, in order to approximate a clean image. Experimental results, both on synthetic and real SAR data, show the method to achieve better performance with respect to state-of-the-art techniques.
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Contributor : Giovanni Chierchia <>
Submitted on : Thursday, February 15, 2018 - 3:19:40 PM
Last modification on : Wednesday, February 26, 2020 - 7:06:07 PM
Long-term archiving on: : Tuesday, May 8, 2018 - 12:52:47 AM


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  • HAL Id : hal-01710036, version 1


Giovanni Chierchia, Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva. SAR image despeckling through convolutional neural networks. IEEE International Geoscience and Remote Sensing Symposium, Jul 2017, Fort Worth, Texas, United States. ⟨hal-01710036⟩



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