PASTORINO , Martina

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...

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...

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...

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

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 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...

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,...

This paper presents a method for the automatic learning of the potentials of a stochastic model, in particular a conditional random field (CRF), in a non-parametric fashion. The proposed model is based on a neural architecture, in order to leverage the modeling capabilities of deep learning approaches to directly learn semantic and spatial...

This work deals with the challenge of semantic segmentation based on deep learning methods in the case of realistic scarce ground truth maps. Exhaustive ground truths usually found in benchmark datasets can be used to train deep learning architectures successfully. On the contrary, real-world ground truths are almost never exhaustive, they are...

In this paper, a novel method to tackle semantic segmentation of very high resolution remote sensing data is presented. Deep learning techniques, such as convolutional neural networks (CNNs) and fully convolutional networks (FCNs), have shown exceptional performances in this task. But the accuracy of their classification depends on the quantity...

Image classification -or semantic segmentation -from input multiresolution imagery is a demanding task. In particular, when dealing with images of the same scene collected at the same time by very different acquisition systems, for example multispectral sensors onboard satellites and unmanned aerial vehicles (UAVs), the difference between the...

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...