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Automatic extraction and classification of vegetation areas from high resolution images in urban areas / Corina Iovan (2007)
Titre : Automatic extraction and classification of vegetation areas from high resolution images in urban areas Type de document : Article/Communication Auteurs : Corina Iovan , Auteur ; Didier Boldo , Auteur ; Matthieu Cord, Auteur ; Mats Erikson, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2007 Conférence : SCIA 2007, 15th Scandinavian Conference on Image Analysis 10/06/2007 14/06/2007 Aalborg Danemark Proceedings Springer Importance : pp 858 - 867 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de la végétation
[Termes IGN] houppier
[Termes IGN] image aérienne
[Termes IGN] image optique
[Termes IGN] texture d'image
[Termes IGN] variable régionalisée
[Termes IGN] zone urbaine
[Termes IGN] zone urbaine denseRésumé : (auteur) This paper presents a complete high resolution aerial-images processing workflow to detect and characterize vegetation structures in high density urban areas. We present a hierarchical strategy to extract, analyze and delineate vegetation areas according to their height. To detect urban vegetation areas, we develop two methods, one using spectral indices and the second one based on a Support Vector Machines (SVM) classifier. Once vegetation areas detected, we differentiate lawns from treed areas by computing a texture operator on the Digital Surface Model (DSM). A robust region growing method based on the DSM is proposed for an accurate delineation of tree crowns. Delineation results are compared to results obtained by a Random Walk region growing technique for tree crown delineation. We evaluate the accuracy of the tree crown delineation results to a reference manual delineation. Results obtained are discussed and the influential factors are put forward. Numéro de notice : C2007-008 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE/INFORMATIQUE Nature : Poster nature-HAL : Poster-avec-CL DOI : 10.1007/978-3-540-73040-8_87 En ligne : http://dx.doi.org/10.1007/978-3-540-73040-8_87 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85951 Multiple support vector machines for land cover change detection: an application for mapping urban extensions / H. Nemmour in ISPRS Journal of photogrammetry and remote sensing, vol 61 n° 2 (November 2006)
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Titre : Multiple support vector machines for land cover change detection: an application for mapping urban extensions Type de document : Article/Communication Auteurs : H. Nemmour, Auteur ; Y. Chibani, Auteur Année de publication : 2006 Article en page(s) : pp 125 - 133 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Alger
[Termes IGN] analyse comparative
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de changement
[Termes IGN] occupation du sol
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] urbanisationRésumé : (Auteur) The reliability of support vector machines for classifying hyperspectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for land cover change detection. First, SVM-based change detection is presented and performed for mapping urban growth in the Algerian capital. Different performance indicators, as well as a comparison with artificial neural networks, are used to support our experimental analysis. In a second step, a combination framework is proposed to improve change detection accuracy. Two combination rules, namely, Fuzzy Integral and Attractor Dynamics, are implemented and evaluated with respect to individual SVMs. Recognition rates achieved by individual SVMs, compared to neural networks, confirm their efficiency for land cover change detection. Furthermore, the relevance of SVM combination is highlighted. Copyright ISPRS Numéro de notice : A2006-531 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2006.09.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2006.09.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28254
in ISPRS Journal of photogrammetry and remote sensing > vol 61 n° 2 (November 2006) . - pp 125 - 133[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-06081 SL Revue Centre de documentation Revues en salle Disponible A novel transductive SVM for semisupervised classification of remote-sensing images / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 2 (November 2006)
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Titre : A novel transductive SVM for semisupervised classification of remote-sensing images Type de document : Article/Communication Auteurs : Lorenzo Bruzzone, Auteur ; M. Chi, Auteur ; Mattia Marconcini, Auteur Année de publication : 2006 Article en page(s) : pp 3363 - 3373 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage dirigé
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification semi-dirigée
[Termes IGN] reconnaissance automatiqueRésumé : (Auteur) This paper introduces a semisupervised classification method that exploits both labeled and unlabeled samples for addressing ill-posed problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference and in particular transductive SVMs (TSVMs). TSVMs exploit specific iterative algorithms which gradually search a reliable separating hyperplane (in the kernel space) with a transductive process that incorporates both labeled and unlabeled samples in the training phase. Based on an analysis of the properties of the TSVMs presented in the literature, a novel modified TSVM classifier designed for addressing ill-posed remote-sensing problems is proposed. In particular, the proposed technique: 1) is based on a novel transductive procedure that exploits a weighting strategy for unlabeled patterns, based on a time-dependent criterion; 2) is able to mitigate the effects of suboptimal model selection (which is unavoidable in the presence of small-size training sets); and 3) can address multiclass cases. Experimental results confirm the effectiveness of the proposed method on a set of ill-posed remote-sensing classification problems representing different operative conditions. Copyright IEEE Numéro de notice : A2006-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.877950 En ligne : https://doi.org/10.1109/TGRS.2006.877950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28250
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 11 Tome 2 (November 2006) . - pp 3363 - 3373[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06111B RAB Revue Centre de documentation En réserve L003 Disponible A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery / L. Zhang in IEEE Transactions on geoscience and remote sensing, vol 44 n° 10 Tome 2 (October 2006)
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Titre : A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery Type de document : Article/Communication Auteurs : L. Zhang, Auteur ; X. Huang, Auteur Année de publication : 2006 Article en page(s) : pp 2950 - 2961 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fusion de données
[Termes IGN] image à haute résolution
[Termes IGN] image multibande
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] pixel
[Termes IGN] précision de la classification
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] reconnaissance de formes
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Shape and spectra are both important features of high spatial resolution remotely sensed (HSRRS) imagery, and they are concrete manifestation of textures on such imagery. This paper presents a spatial feature index, pixel shape index (PSI), to describe the shape feature in a local area surrounding a pixel. PSI is a pixel-based feature which measures the gray similarity distance in every direction. As merely the shape feature is inadequate for classifying HSRRS imagery, a transformed spectral feature extracted by independent component analysis is added to the input vectors of our classifier, and this replaces the original multispectral bands. Meanwhile, a fast fusion algorithm that integrates both shape and spectral features using the support vector machine has been developed to interpret the complex input vectors. The results by PSI are compared with some spatial features extracted using wavelet transform, gray level co-occurrence matrix, and the length–width extraction algorithm to test its effectiveness. The experiments demonstrate that PSI is capable of describing shape features effectively and result in more accurate classifications than other methods. While it is found that spectral and shape features can complement each other and their integration can improve classification accuracy, the transformed spectral components are also found to be more suitable for classification. Copyright IEEE Numéro de notice : A2006-504 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.876704 En ligne : https://doi.org/10.1109/TGRS.2006.876704 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28228
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 10 Tome 2 (October 2006) . - pp 2950 - 2961[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06101B RAB Revue Centre de documentation En réserve L003 Disponible Training set size requirements for the classification of a specific class / Giles M. Foody in Remote sensing of environment, vol 104 n° 1 (15/09/2006)
[article]
Titre : Training set size requirements for the classification of a specific class Type de document : Article/Communication Auteurs : Giles M. Foody, Auteur ; A. Mathur, Auteur ; et al., Auteur Année de publication : 2006 Article en page(s) : pp 1 - 14 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Gossypium (genre)
[Termes IGN] Inde
[Termes IGN] intelligence artificielle
[Termes IGN] réduction géométriqueRésumé : (Auteur) The design of the training stage of a supervised classification should account for the properties of the classifier to be used. Consideration of the way the classifier operates may enable the training stage to be designed in a manner which ensures that the aim of the classification is satisfied with the use of a small, inexpensive, training set. It may, therefore, be possible to reduce the training set size requirements from that generally expected with the use of standard heuristics. Substantial reductions in training set size may be possible if interest is focused on a single class. This is illustrated for mapping cotton in north-western India by support vector machine type classifiers. Four approaches to reducing training set size were used: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes and the adoption of a one-class classifier. All four approaches were able to reduce the training set size required considerably below that suggested by conventional widely used heuristics without significant impact on the accuracy with which the class of interest was classified. For example, reductions in training set size of not, vert, similar 90% from that suggested by a conventional heuristic are reported with the accuracy of cotton classification remaining nearly constant at not, vert, similar 95% and not, vert, similar 97% from the user's and producer's perspectives respectively. Copyright Elsevier Numéro de notice : A2006-392 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.03.004 En ligne : https://doi.org/10.1016/j.rse.2006.03.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28116
in Remote sensing of environment > vol 104 n° 1 (15/09/2006) . - pp 1 - 14[article]A support vector method for anomaly detection in hyperspectral imagery / Amit Banerjee in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)PermalinkSome issues in the classification of DAIS hyperspectral data / M. Pal in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)PermalinkClassification of fully polarimetric SAR data for land use cartography / Cédric Lardeux in Revue Française de Photogrammétrie et de Télédétection, n° 182 (Juin 2006)PermalinkPermalinkA statistical self-organizing learning system for remote sensing classification / H.M. Chi in IEEE Transactions on geoscience and remote sensing, vol 43 n° 8 (August 2005)PermalinkLand covers update by supervised classification of segmented ASTER images / A.R.S. Marcal in International Journal of Remote Sensing IJRS, vol 26 n° 7 (April 2005)PermalinkPartially supervised classification of remote sensing images through SVM-based probability density estimation / P. Mantero in IEEE Transactions on geoscience and remote sensing, vol 43 n° 3 (March 2005)PermalinkRoad extraction using SVM and image segmentation / M. Song in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 12 (December 2004)PermalinkClassification 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)Permalink