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Your search keyword '"Sebastiano B. Serpico"' showing total 305 results
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305 results on '"Sebastiano B. Serpico"'

1. Multimodal Fusion of Mobility Demand Data and Remote Sensing Imagery for Urban Land-Use and Land-Cover Mapping

Author

Martina Pastorino, Federico Gallo, Angela Di Febbraro, Gabriele Moser, Nicola Sacco, and Sebastiano B. Serpico

Subjects
urban land-use mapping, data fusion, Markov random fields, transport zones, mobility demand, Science
Abstract

This paper aims at exploring the potentiality of the multimodal fusion of remote sensing imagery with information coming from mobility demand data in the framework of land-use mapping in urban areas. After a discussion on the function of mobility demand data, a probabilistic fusion framework is developed to take advantage of remote sensing and transport data, and their joint use for urban land-use and land-cover applications in urban and surrounding areas. Two different methods are proposed within this framework, the first based on pixelwise probabilistic decision fusion and the second on the combination with a region-based multiscale Markov random field. The experimental validation is conducted on a case study associated with the city of Genoa, Italy.

Published
2022
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2. Multisensor and Multiresolution Remote Sensing Image Classification through a Causal Hierarchical Markov Framework and Decision Tree Ensembles

Author

Martina Pastorino, Alessandro Montaldo, Luca Fronda, Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico, and Josiane Zerubia

Subjects
multimodal data fusion, multiresolution and multisensor fusion, causal Markov model, hierarchical Markov random field, Markov mesh random field, Markov chain, Science
Abstract

In this paper, a hierarchical probabilistic graphical model is proposed to tackle joint classification of multiresolution and multisensor remote sensing images of the same scene. This problem is crucial in the study of satellite imagery and jointly involves multiresolution and multisensor image fusion. The proposed framework consists of a hierarchical Markov model with a quadtree structure to model information contained in different spatial scales, a planar Markov model to account for contextual spatial information at each resolution, and decision tree ensembles for pixelwise modeling. This probabilistic graphical model and its topology are especially fit for application to very high resolution (VHR) image data. The theoretical properties of the proposed model are analyzed: the causality of the whole framework is mathematically proved, granting the use of time-efficient inference algorithms such as the marginal posterior mode criterion, which is non-iterative when applied to quadtree structures. This is mostly advantageous for classification methods linked to multiresolution tasks formulated on hierarchical Markov models. Within the proposed framework, two multimodal classification algorithms are developed, that incorporate Markov mesh and spatial Markov chain concepts. The results obtained in the experimental validation conducted with two datasets containing VHR multispectral, panchromatic, and radar satellite images, verify the effectiveness of the proposed framework. The proposed approach is also compared to previous methods that are based on alternate strategies for multimodal fusion.

Published
2021
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6. A Markovian Approach to Unsupervised Change Detection with Multiresolution and Multimodality SAR Data

Author

David Solarna, Gabriele Moser, and Sebastiano B. Serpico

Subjects
synthetic aperture radar (SAR), multiresolution data fusion, multimodality data fusion, generalized Gaussian, maximum likelihood (ML) estimation, minimum mean squared error (MMSE) estimation, Gram–Charlier approximation, Markov random fields (MRF), graph cuts, Science
Abstract

In the framework of synthetic aperture radar (SAR) systems, current satellite missions make it possible to acquire images at very high and multiple spatial resolutions with short revisit times. This scenario conveys a remarkable potential in applications to, for instance, environmental monitoring and natural disaster recovery. In this context, data fusion and change detection methodologies play major roles. This paper proposes an unsupervised change detection algorithm for the challenging case of multimodal SAR data collected by sensors operating at multiple spatial resolutions. The method is based on Markovian probabilistic graphical models, graph cuts, linear mixtures, generalized Gaussian distributions, Gram⁻Charlier approximations, maximum likelihood and minimum mean squared error estimation. It benefits from the SAR images acquired at multiple spatial resolutions and with possibly different modalities on the considered acquisition times to generate an output change map at the finest observed resolution. This is accomplished by modeling the statistics of the data at the various spatial scales through appropriate generalized Gaussian distributions and by iteratively estimating a set of virtual images that are defined on the pixel grid at the finest resolution and would be collected if all the sensors could work at that resolution. A Markov random field framework is adopted to address the detection problem by defining an appropriate multimodal energy function that is minimized using graph cuts.

Published
2018
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50. Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures.

Author

Sebastiano B. Serpico, Lorenzo Bruzzone, Giovanni Corsini, William J. Emery, Paolo Gamba, Andrea Garzelli, Grégoire Mercier, Josiane Zerubia, Nicola Acito, Bruno Aiazzi, Francesca Bovolo, Fabio Dell'Acqua, Michaela De Martino, Marco Diani, Vladimir A. Krylov, Gianni Lisini, Carlo Marin, Gabriele Moser, Aurélie Voisin, and Claudia Zoppetti

Published
2012
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