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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,...
This work deals with the fusion of SAR and optical data for land cover monitoring. We first propose to use co-registered optical data as a guide for nonlocal SAR image despeckling. Then, we fuse filtered Sentinel-1 SAR data with optical Sentinel-2 data for land-use classification. Experiments show that using optical-driven despeckled SAR data largely improves classification accuracy w.r.t using the...
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