MOSER G.

The current progress of remote sensing systems, based on airborne and spaceborne platforms and involving active and passive sensors, provides an unprecedented wealth of information about the Earth surface for environmental monitoring, sustainable resource management, disaster prevention, emergency response, and defense. In this framework,...

Current and forthcoming sensor technologies and space missions are providing remote sensing scientists and practitioners with an increasing wealth and variety of data modalities. They encompass multisensor, multiresolution, multiscale, multitemporal, multipolarization, and multifrequency imagery. While they represent remarkable opportunities...

In this paper, the problem of the spatial-spectral classification of very high-resolution optical images is addressed using a kernel- A nd region-based approach. A novel method based on integrating region-based or object-based information into a kernel machine is developed. A Gaussian process model is used to characterize each segment in a...

Current satellite missions (e.g., COSMO-SkyMed, Sentinel-1) collect single- or multipolarimetric synthetic aperture radar (SAR) images with multiple spatial resolutions and possibly short revisit times. The availability of heterogeneous data requires effective methods able to exploit all the available information. In the context of...

This paper addresses the challenges of supervised semantic segmentation using Polarimetric Synthetic Aperture Radar (PolSAR) data for land cover mapping. We extend previous approaches relying on spatial-contextual classifier based on Support Vector Machines (SVMs) and Markov Random Field (MRF) models. The kernel used in this work extends a...

Change detection (CD) is among the most important tools in natural disaster monitoring. Special emphasis is on heterogeneous CD methods, which allow for a faster response. In this paper, we propose a novel heterogeneous CD method tailored at working with image domains of very different dimensionality, which allows for a greater applicational...

Tactile data processing and analysis is still essentially an open challenge. In this framework, we demonstrate a method to achieve touch modality classification using pre-trained convolutional neural networks (CNNs). The 3D tensorial tactile data generated by real human interactions on an electronic skin (E-Skin) are transformed into 2D images....

Convolutional Neural Networks (CNNs) have become the new standard for semantic segmentation of very high resolution images. But as for other methods, the map accuracy depends on the quantity and quality of ground truth used to train them. Having densely annotated data, i.e. a detailed, pixel-level ground truth (GT), allows obtaining effective...

The first two steps of performance analysis and design of transport systems usually consist of identifying the best spatial discretization of the considered study area, namely zoning, and of the relevant Origin-Destination (OD) matrix. In this framework, zones are generally delimited by physical obstacles such as rivers, transport...

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The automatic registration of multisensor remote sensing images is a highly challenging task due to the inherently different physical, statistical, and textural properties of the input data. In the present paper, this problem is addressed in the case of optical-SAR images by proposing a novel method based on deep learning and area-based...

The automatic registration of image pairs composed of optical and synthetic aperture radar (SAR) images is a highly challenging task because of the inherently different physical, statistical, and textural properties of the input data. Information-theoretic measures capable of comparing local intensity distributions are often used for...