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Communication Dans Un Congrès Année : 2017

SAR image despeckling through convolutional neural networks

Résumé

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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Dates et versions

hal-01710036 , version 1 (15-02-2018)

Identifiants

  • HAL Id : hal-01710036 , version 1

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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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