SAR image despeckling through convolutional neural networks

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 nois...

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Bibliographic Details
Published inIEEE International Geoscience and Remote Sensing Symposium proceedings pp. 5438 - 5441
Main Authors Chierchia, G., Cozzolino, D., Poggi, G., Verdoliva, L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2017
Subjects
convolutional neural networks
Indexes
multiplicative noise
Noise measurement
Remote sensing
SAR
Speckle
Synthetic aperture radar
Training
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Summary: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.
ISSN:2153-7003
DOI:10.1109/IGARSS.2017.8128234