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Auteur Lorenzo Bruzzone |
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An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images / Y. Bazi in IEEE Transactions on geoscience and remote sensing, vol 43 n° 4 (April 2005)
[article]
Titre : An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images Type de document : Article/Communication Auteurs : Y. Bazi, Auteur ; Lorenzo Bruzzone, Auteur ; F. Melgani, Auteur Année de publication : 2005 Article en page(s) : pp 874 - 887 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] chatoiement
[Termes IGN] détection de changement
[Termes IGN] distribution de Gauss
[Termes IGN] filtrage numérique d'image
[Termes IGN] image ERS-SAR
[Termes IGN] image multitemporelle
[Termes IGN] image radar
[Termes IGN] seuillage d'imageRésumé : (Auteur) 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 filtering; 2) a comparison between multitemporal images carried out according to a standard log-ratio operator; and 3) a novel approach to the automatic analysis of the log-ratio image for generating the change-detection map. The first step aims at reducing the speckle noise in a controlled way in order to maximize the discrimination capability between changed and unchanged classes. In the second step, the two filtered multitemporal images are compared to generate a log-ratio image that contains explicit information on changed areas. The third step produces the change-detection map according to a thresholding procedure based on a reformulation of the Kittler-Illingworth (KI) threshold selection criterion. In particular, the modified KI criterion is derived under the generalized Gaussian assumption for modeling the distributions of changed and unchanged classes. This parametric model was chosen because it is capable of better fitting the conditional densities of classes in the log-ratio image. In order to control the filtering step and, accordingly, the effects of the filtering process on change-detection accuracy, we propose to identify automatically the optimal number of despeckling filter iterations [Step 1)] by analyzing the behavior of the modified KI criterion. This results in a completely automatic and self-consistent change-detection approach that avoids the use of empirical methods for the selection of the best number of filtering iterations. Experiments carried out on two sets of multitemporal images (characterized by different levels of speckle noise) acquired by the European Remote Sensing 2 satellite SAR sensor confirm the effectiveness of the proposed unsupervised approach, which results in change-detection accuracies very similar to those that can be achieved by a manual supervised thresholding. Numéro de notice : A2005-194 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.842441 En ligne : https://doi.org/10.1109/TGRS.2004.842441 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27331
in IEEE Transactions on geoscience and remote sensing > vol 43 n° 4 (April 2005) . - pp 874 - 887[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-05042 RAB Revue Centre de documentation En réserve L003 Disponible Robust multiple estimator systems for the analysis of biophysical parameters from remotely sensed data / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 43 n° 1 (January 2005)
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Titre : Robust multiple estimator systems for the analysis of biophysical parameters from remotely sensed data Type de document : Article/Communication Auteurs : Lorenzo Bruzzone, Auteur ; F. Melgani, Auteur Année de publication : 2005 Article en page(s) : pp 159 - 174 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] estimateur
[Termes IGN] Perceptron multicouche
[Termes IGN] régression
[Termes IGN] réseau neuronal artificiel
[Termes IGN] télédétection spatiale
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) In this paper, an approach based on multiple estimator systems (MESs) for the estimation of biophysical parameters from remotely sensed data is proposed. The rationale behind the proposed approach is to exploit the peculiarities of an ensemble of different estimators in order to improve the robustness (and in some cases the accuracy) of the estimation process. The proposed MESs can be implemented in two conceptually different ways. One extends the use of an approach previously proposed in the regression literature to the estimation of biophysical parameters from remote sensing data. This approach integrates the estimates obtained from the different regression algorithms making up the ensemble by a direct linear combination (combination-based approach). The other consists of a novel approach that provides as output the estimate obtained by the regression algorithm (included in the ensemble) characterized by the highest expected accuracy in the region of the feature space associated with the considered pattern (selection-based approach). This estimator is identified based on a proper partition of the feature space. The effectiveness of the proposed approach has been assessed on the problem of estimating water quality parameters from multispectral remote sensing data. In particular, the presented MES-based approach has been evaluated by considering different operational conditions where the single estimators included in the ensemble are: 1) based on the same or on different regression methods; 2) characterized by different tradeoffs between correlated errors and accuracy of the estimates; 3) trained on samples affected or not by measurement errors. In the definition of the ensemble particular attention is devoted to support vector machines (SVMs), which are a promising approach to the solution of regression problems. In particular, a detailed experimental analysis on the effectiveness of SVMs for solving the considered estimation problem is presented. The experimental results point out that the SVM method is effective and that the proposed MES approach is capable of increasing both the robustness and accuracy of the estimation process. Numéro de notice : A2005-060 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.839818 En ligne : https://doi.org/10.1109/TGRS.2004.839818 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27198
in IEEE Transactions on geoscience and remote sensing > vol 43 n° 1 (January 2005) . - pp 159 - 174[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-05011 RAB Revue Centre de documentation En réserve L003 Disponible Classification of hyperspectral remote sensing images with support vector machines / F. Melgani in IEEE Transactions on geoscience and remote sensing, vol 42 n° 8 (August 2004)
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Titre : Classification of hyperspectral remote sensing images with support vector machines Type de document : Article/Communication Auteurs : F. Melgani, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2004 Article en page(s) : pp 1778 - 1790 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classification barycentrique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectraleRésumé : (Auteur) This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines (SVMs). First, we propose a theoretical discussion and experimental analysis aimed at understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces. Then, we assess the effectiveness of SVMs with respect to conventional feature-reduction-based approaches and their performances in hypersubspaces of various dimensionalities. To sustain such an analysis, the performances of SVMs are compared with those of two other nonparametric classifiers (i.e., radial basis function neural networks and the K-nearest neighbor classifier). Finally, we study the potentially critical issue of applying binary SVMs to multiclass problems in hyperspectral data. In particular, four different multiclass strategies are analyzed and compared: the one-against-all, the one-against-one, and two hierarchical tree-based strategies. Different performance indicators have been used to support our experimental studies in a detailed and accurate way, i.e., the classification accuracy, the computational time, the stability to parameter setting, and the complexity of the multiclass architecture. The results obtained on a real Airborne Visible/Infrared Imaging Spectroradiometer hyperspectral dataset allow to conclude that, whatever the multiclass strategy adopted, SVMs are a valid and effective alternative to conventional pattern recognition approaches (feature-reduction procedures combined with a classification method) for the classification of hyperspectral remote sensing data. Numéro de notice : A2004-389 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.831865 En ligne : https://doi.org/10.1109/TGRS.2004.831865 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26916
in IEEE Transactions on geoscience and remote sensing > vol 42 n° 8 (August 2004) . - pp 1778 - 1790[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-04081 RAB Revue Centre de documentation En réserve L003 Disponible An advanced system for the automatic classification of multitemporal SAR images / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 42 n° 6 (June 2004)
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Titre : An advanced system for the automatic classification of multitemporal SAR images Type de document : Article/Communication Auteurs : Lorenzo Bruzzone, Auteur ; Mattia Marconcini, Auteur ; et al., Auteur Année de publication : 2004 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification automatique
[Termes IGN] classification par réseau neuronal
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] extraction automatique
[Termes IGN] image ERS-SAR
[Termes IGN] image multitemporelle
[Termes IGN] reconnaissance de formesRésumé : (Auteur) A novel system for the classification of multitemporal synthetic aperture radar (SAR) images is presented. It has been developed by integrating an analysis of the multitemporal SAR signal physics with a pattern recognition approach. The system is made up of a feature-extraction module and a neural-network classifier, as well as a set of standard preprocessing procedures. The feature-extraction module derives a set of features from a series of multitemporal SAR images. These features are based on the concepts of long-term coherence and backscattering temporal variability and have been defined according to an analysis of the multitemporal SAR signal behavior in the presence of different land-cover classes. The neural-network classifier (which is based on a radial basis function neural architecture) properly exploits the multitemporal features for producing accurate land-cover maps. Thanks to the effectiveness of the extracted features, the number of measures that can be provided as input to the classifier is significantly smaller than the number of available multitemporal images. This reduces the complexity of the neural architecture (and consequently increases the generalization capabilities of the classifier) and relaxes the requirements relating to the number of training patterns to be used for classifier learning. Experimental results (obtained on a multitemporal series of European Remote Sensing 1 satellite SAR images) confirm the effectiveness of the proposed system, which exhibits both high classification accuracy and good stability versus parameter settings. These results also point out that properly integrating a pattern recognition procedure (based on machine learning) with an accurate feature extraction phase (based on the SAR sensor physics understanding) represents an effective approach to SAR data analysis. Numéro de notice : A2004-264 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.826821 En ligne : https://doi.org/10.1109/TGRS.2004.826821 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26791
in IEEE Transactions on geoscience and remote sensing > vol 42 n° 6 (June 2004)[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-04061 RAB Revue Centre de documentation En réserve L003 Disponible A multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 40 n° 9 (September 2002)
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Titre : A multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps Type de document : Article/Communication Auteurs : Lorenzo Bruzzone, Auteur ; R. Cossu, Auteur Année de publication : 2002 Article en page(s) : pp 1984 - 1996 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification non dirigée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par réseau neuronal
[Termes IGN] image multitemporelle
[Termes IGN] mise à jour cartographiqueRésumé : (Auteur) A system for a regular updating of landcover maps is proposed that is based on the use of multitemporal remote sensing images. Such a system is able to address the updating problem under the realistic but critical constraint that, for the image to be classified (i.e., the most recent of the considered multitemporal dataset no ground truth information is available. The system is composed of an ensemble of partially unsupervised classifiers integrated in a multipleclassifier architecture. Each classifier of the ensemble exhibits the following novel characteristics: 1) it is developed in the framework of the cascade-classification approach to exploit the temporal correlation existing between images acquired at different times in the considered area; and 2) it is based on a partially unsupervised methodology capable of accomplishing the classification process under the aforementioned critical constraint. Both a parametric maximumlikelihood (ML) classification approach and a nonparametric radial basis function (RBF) neuralnetwork classification approach are used as basic methods for the development of partially unsupervised cascade classifiers. In addition, in order to generate an effective ensemble of classification algorithms, hybrid ML and RBF neuralnetwork cascade classifiers are defined by exploiting the characteristics of the cascadeclassification methodology. The results yielded by the different classifiers are combined by using standard unsupervised combination strategies. This allows the definition of a robust and accurate partially unsupervised classification system capable of analyzing a wide typology of remote sensing data (e.g., images acquired by passive sensors, synthetic aperture radar images, and multisensor and multisource data). Experimental results obtained on a real multitemporal and multisource dataset confirm the effectiveness of the proposed system. Numéro de notice : A2002-287 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2002.803794 En ligne : https://doi.org/10.1109/TGRS.2002.803794 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22198
in IEEE Transactions on geoscience and remote sensing > vol 40 n° 9 (September 2002) . - pp 1984 - 1996[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-02081 RAB Revue Centre de documentation En réserve L003 Disponible A new search algorithm for feature selection in hyperspectral remote sensing images / S.B. Serpico in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)Permalink