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SAR image despeckling through convolutional neural networks

Giovanni Cierchia 1      Davide Cozzolino 2      Luisa Verdoliva 2      Giovanni Poggi 2     
1 Universiè Paris Est, ESIEE Paris, France.
2 Department of Industrial Engineering, Università Federico II di Napoli, Italy


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 multitemporal 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.


author={G. Chierchia and D. Cozzolino and G. Poggi and L. Verdoliva},
booktitle={2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, 
title={SAR image despeckling through convolutional neural networks},