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—In this paper, we present a novel automatic and unsupervised change-detection approach specifically oriented to the analysis of multitemporal single-channel single-polarization synthetic aperture radar (SAR) images. This approach is based on a closed-loop process made up of three main steps: 1) a novel preprocessing based on a controlled adaptive iterative(More)
The aim of this paper is twofold. First, we present a thorough experimental study to show the superiority of the generalization capability of the support vector machine (SVM) approach in the automatic classification of electrocardiogram (ECG) beats. Second, we propose a novel classification system based on particle swarm optimization (PSO) to improve the(More)
—Gaussian processes (GPs) represent a powerful and interesting theoretical framework for Bayesian classification. Despite having gained prominence in recent years, they remain an approach whose potentialities are not yet sufficiently known. In this paper, we propose a thorough investigation of the GP approach for classifying multisource and hyperspectral(More)
—Recent remote sensing literature has shown that support vector machine (SVM) methods generally outperform traditional statistical and neural methods in classification problems involving hyperspectral images. However, there are still open issues that, if suitably addressed, could allow further improvement of their performances in terms of classification(More)
In this paper, a new feature named heartbeat shape (HBS) is proposed for ECG-based biometrics. HBS is computed from the morphology of segmented heartbeats. Computation of the feature involves three basic steps: 1) resampling and normalization of a heartbeat; 2) reduction of matching error; and 3) shift invariant transformation. In order to construct both(More)
—In this paper, a novel semisupervised regression approach is proposed to tackle the problem of biophysical parameter estimation that is constrained by a limited availability of training (labeled) samples. The main objective of this approach is to increase the accuracy of the estimation process based on the support vector machine (SVM) technique by(More)