ZERUBIA J.

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, a novel method to deal with the semantic segmentation of very high resolution remote sensing data is presented. Recent advances in deep learning (DL), especially convolutional neural networks (CNNs) and fully convolutional networks (FCNs), have shown outstanding performances in this task. However, the map accuracy depends on the...

This paper addresses the semantic segmentation of synthetic aperture radar (SAR) images through the combination of ful-ly convolutional networks (FCN s), hierarchical probabilistic graphical models (PGMs), and decision tree ensembles. The idea is to incorporate long-range spatial information together with the multiresolution information...

The method presented in this paper for semantic segmentation of multiresolution remote sensing images involves convolutional neural networks (CNNs), in particular fully convolutional networks (FCNs), and hierarchical probabilistic graphical models (PGMs). These approaches are combined to overcome the limitations in classification accuracy of...

Deep learning (DL) is currently the dominant approach to image classification and segmentation, but the performances of DL methods are remarkably influenced by the quantity and quality of the ground truth (GT) used for training. In this article, a DL method is presented to deal with the semantic segmentation of very-high-resolution (VHR)...

This paper presents a novel semantic segmentation method of very high resolution remotely sensed images based on fully convolutional networks (FCNs) and feedforward neural networks (FFNNs). The proposed model aims to exploit the intrinsic multiscale information extracted at different convolutional blocks in an FCN by the integration of FFNNs,...

The problem of the semantic segmentation of multimodal images is characterized by the challenge of jointly exploiting information deriving from images possibly acquired at different spatial resolutions, frequencies, and bands. This paper proposes to address this task in the case of multimission synthetic aperture radar (SAR) images, through a...

The availability of multimodal remotely sensed images calls for the development of methods capable to jointly exploit the information deriving from images acquired at different spatial resolutions, frequencies, and bands, taking advantage from their possible complementary features. This letter proposes to address this task in the case of...

This paper tackles the semantic segmentation of zones affected by forest fires by the introduction of methods fusing multimodal imagery collected from unmanned aerial vehicles (UAVs) and satellite platforms. The multiresolution fusion task is especially challenging in this case because the difference between the involved spatial resolutions is...

In this paper, a multiscale Markov framework is proposed in order to address the problem of the classification of multiresolution and multisensor remotely sensed data. The proposed framework makes use of a quadtree to model the interactions across different spatial resolutions and a Markov model with respect to a generic total order relation to...

In this paper, the problem of the classification of multiresolution and multisensor remotely sensed data is addressed by proposing a multiscale Markov mesh model. Multiresolution and multisensor fusion are jointly achieved through an explicitly hierarchical probabilistic graphical classifier, which uses a quadtree structure to model the...

In this paper, we address the problem of the joint classification of multiple images acquired on the same scene at different spatial resolutions. From an application viewpoint, this problem is of importance in several contexts, including, most remarkably, satellite and aerial imagery. From a methodological perspective, we use a probabilistic...

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...

This chapter presents an overview of the major concepts and of the recent literature in the area of remote sensing data fusion. It describes two advanced methods for the joint supervised classification of multimission image time series, including multisensor optical and Synthetic Aperture Radar (SAR) components acquired at multiple spatial...