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Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained ...
In this paper, we study the applicability of active learning (AL) in operative scenarios. More particularly, we consider the well-known contradiction between the AL heuristics, which rank the pixels according to their uncertainty, and the user's confidence ...
We propose a semiautomatic procedure to generate land cover maps from remote sensing images. The proposed algorithm starts by building a hierarchical clustering tree, and exploits the most coherent pixels with respect to the available class information. Fo ...
La télédétection présente de nombreux avantages pour caractériser l’environnement urbain. De nombreux types de capteurs - satellites ou aéroportés - sont aujourd’hui à disposition et permettent d’obtenir des informations précieuses capables d’enrichir nos ...
In recent years, several studies have been published about the smart definition of training set using active learning algorithms. However, none of these works consider the contradiction between the active learning methods, which rank the pixels according t ...
The analysis of multi-modal and multi-sensor images is nowadays of paramount importance for Earth Observation (EO) applications. There exist a variety of methods that aim at fusing the different sources of information to obtain a compact representation of ...
When dealing with change detection problems, information about the nature of the changes is often unavailable. In this paper we propose a solution to perform unsupervised change detection based on nonlinear support vector clustering. We build a series of n ...
The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution opt ...
When dealing with supervised target detection, the acquisition of labeled samples is one of the most critical phases: the samples must be yet representative of the class of interest, but must also be found among a vast majority of non-target examples. More ...
Locating new wind farms is of crucial importance for energy policies of the next decade. To select the new location, an accurate picture of the wind fields is necessary. However, characterizing wind fields is a difficult task, since the phenomenon is highl ...
