SERPICO , Sebastiano B.

This paper addresses the problem of exploiting very high-resolution multifrequency SAR data collected by the COSMO-SkyMed and RADARSAT-2 missions to support risk monitoring and assessment in urban and suburban areas. The proposed approach aims at taking benefit from the synergy between the two SAR data sources to optimize the accuracy of...

The k-NN rules and their modifications offer usually very good performance. The main disadvantage of the k-NN rules is the necessity of keeping the reference set (i.e. training set) in the computer memory. Numerous algorithms for the reference set reduction have been already created. They concern the 1-NN rule and are based on the consistency...

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,...

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In this paper we investigate a new hierarchical method for high resolution remotely sensed image classification. The proposed approach integrates an explicit hierarchical graph-based classifier, which uses a quad-tree structure to model multiscale interactions, and a symmetric Markov mesh random field to deal with pixelwise contextual...

This letter proposes two methods for the supervised classification of multisensor optical and synthetic aperture radar images with possibly different spatial resolutions. Both the methods are formulated within a unique framework based on hierarchical Markov random fields. Distinct quad-trees associated with the individual information sources...

This paper introduces a method to automatically learn the unary and pairwise potentials of a conditional random field (CRF) from the input data in a non-parametric fashion, within the framework of the semantic segmentation of remote sensing images. The proposed model is based on fully convolutional networks (FCNs) and fully connected neural...

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This paper addresses the problem of semi-automatic image registration on planetary images. A joint feature-based and area-based approach is proposed. Firstly, the most relevant craters are extracted from the two images to register, and then, registration is performed in two steps. The first step matches the craters extracted from the images...

The availability of synthetic aperture radar (SAR) data with high spatial resolution offers great potential for environmental monitoring due to the insensitivity of SAR to atmospheric and sunlight-illumination conditions. In this paper, an unsupervised change detection method for SAR images at medium to high resolution is proposed. The image...

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multisensor and multitemporal images. These multiple data represent a precious asset, as they allow the study of target spectral responses and of changes in the surface structure. Because of their variety, they also require...

In this paper we address the problem of urban optical imagery classification by developing a convolutional neural network (CNN) approach. We design a custom CNN that operates on local patches in order to produce dense pixel-level classification map. In this work we focus on a comprehensive dataset of 2.5-meter SPOT-5 imagery acquired at...

Hyperspectral images in the thermal infrared range are attracting increasing attention in the remote sensing field. Nonetheless, the generation of land cover maps using this innovative kind of remote sensing data has been scarcely studied so far. The aim of this article is to experimentally investigate the potential of various supervised...

The development of fault diagnosis systems able to early detect and identify any malfunctioning is of great importance towards the diffusion of energy conversion plants based on solid oxide fuel cells. Because the traditional model-based schemes for the diagnosis demonstrated a poor fault identification capability (especially when many...

In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on Synthetic Aperture Radar (SAR) data, this modelling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In the present report, an...

In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on synthetic aperture radar (SAR) data, this modeling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In this paper, an innovative...

In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of the pixel intensities. In the current research report, we address the problem of parametric probability density function (PDF) estimation in the context of Synthetic Aperture Radar (SAR) amplitude data...

In remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of the pixel intensities. This paper deals with the problem of probability density function (pdf) estimation in the context of synthetic aperture radar (SAR) amplitude data analysis. Several theoretical and heuristic...