No abstract

Interferometric synthetic aperture radar (SAR) (InSAR) systems allow 3-D reconstruction of observed scene. In this paper, an innovative approach for phase unwrapping and digital elevation model (DEM) generation using multichannel InSAR data is presented. The proposed algorithm, exploiting both the amplitude and phase of the available complex data, is able to unwrap and simultaneously regularize the observed data. In particular, the exploitation of amplitude data within the unwrapping chain helps in preserving sharp discontinuities typical of urban areas. As a result, the technique provides accurate DEM reconstructions. For this aim, a Markovian approach, together with a new graph-cut-based optimization algorithm, has been considered. The method has been developed specifically to work in urban areas with very high resolution InSAR image stacks, being able to automatically compensate possible phase offsets. Results on both simulated and real case studies are reported, showing the effectiveness of the metho

Markovian approaches have proven to be effective for solving the multichannel phase-unwrapping (PU) problem, particularly when dealing with noisy data and big discontinuities. This letter presents a Markovian approach to solve the PU problem based on a new a priori model, the total variation, and graph-cut-based optimization algorithms. The proposed method turns out to be fast, simple, and robust. Moreover, compared with other approaches, the proposed algorithm is able to unwrap and restore the solution at the same time, without any additional filtering. A set of experimental results on both simulated and real data illustrates the effectiveness of our approach

Field inhomogeneities in Magnetic Resonance Imaging (MRI) can cause blur or image distortion as they produce off-resonance frequency at each voxel. These effects can be corrected if an accurate field map is available. Field maps can be estimated starting from the phase of multiple complex MRI data sets. In this paper we present a technique based on statistical estimation in order to reconstruct a field map exploiting two or more scans. The proposed approach implements a Bayesian estimator in conjunction with the Graph Cuts optimization method. The effectiveness of the method has been proven on simulated and real data.

No abstract

We introduce a novel approach for scanned document representation to perform field extraction. It allows the simultaneous encoding of the textual, visual and layout information in a 3D matrix used as an input to a segmentation model. We improve the recent Chargrid and Wordgrid [1] models in several ways, first by taking into account the visual modality, then by boosting its robustness in regards to small datasets while keeping the inference time low. Our approach is tested on public and private document-image datasets, showing higher performances compared to the recent state-of-the-art methods.

No abstract

With the development of remotely-sensed multisensor satellites like Pleiades Cosmo-Skymed that have the particularity of providing both SAR and optic data, new techniques in image processing are needed. These techniques must take into account the complementarities and differences in nature of these data. A preliminary operation for advanced techniques that use multisensor images such as fusion, classification, etc. is registration. In the case of SAR and optic data, we can do automatic registration if we exactly know the sensor parameters and have a digital terrain model (DTM) or a digital elevation model (DEM) at our disposal. If we do not have an exact knowledge of these parameters, the registration becomes difficult. Another approach to achieve the automatic registration which does not need sensor parameters will rely on comparison measures between both data. In this paper, we present a comparison of several similarity measures between multisensor SAR and optic images used in matching algorithms. An evaluation of these measures for synthetic data based on their distributions is given. Then results on real images are analyzed.