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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...
A new algorithm is proposed for the detection and accurate localization of copy-move video forgeries. Major ingredients of the proposed algorithm are i) noise-resilient rotation-invariant features, ii) dense-field matching over the whole video by means of a suitably modified fast algorithm for approximate nearest neighbor search, iii) ad hoc fast post-processing for forgery detection and false alarm...
Digital camera identification is a very active research area, with important applications in the forensics field. Several approaches have been proposed in recent years for this task. One of the most promising is based on the estimation of the sensor noise pattern, used as a sort of camera fingerprint. However, a clever attacker can estimate a camera fingerprint and use it maliciously: this calls for...
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