Publications about Support Vector Machines

Result of the query in the list of publications :

• 1 PhD Thesis and Habilitation
• 9 Conference articles
• 2 Technical and Research Reports

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PhD Thesis and Habilitation

1 - Détection de zones brûlées après un feu de forêt à partir d'une seule image satellitaire SPOT 5 par techniques SVM. O. Zammit. PhD Thesis, Universite de Nice Sophia Antipolis, September 2008. Keywords : Classification, Satellite images, Burnt areas, Forest fires, Support Vector Machines, Region Growing. Copyright : @PHDTHESIS{zammit_these_08, author = {Zammit, O.}, title = {Détection de zones brûlées après un feu de forêt à partir d'une seule image satellitaire SPOT 5 par techniques SVM}, year = {2008}, month = {September}, school = {Universite de Nice Sophia Antipolis}, url = {http://tel.archives-ouvertes.fr/tel-00345683/fr/}, keyword = {Classification, Satellite images, Burnt areas, Forest fires, Support Vector Machines, Region Growing} } Résumé : Cette thèse aborde le problème de cartographie de zones brûlées à partir d'images satellitaires haute résolution. Nos modèles reposent sur le traitement d'une seule image SPOT 5, acquise après le feu afin de détecter automatiquement les zones brûlées. Le modèle est fondé sur les Séparateurs à Vaste Marge (SVM), une technique de classification supervisée qui a démontré une meilleure précision et une meilleure capacité de généralisation que les algorithmes de classification plus traditionnels. Concernant notre problème de détection, les différentes zones brûlées possèdent des caractéristiques spectrales assez similaires, au contraire des zones non brûlées (végétation, routes, eau, zones urbaines, nuage, ombre...) dont les caractéristiques spectrales varient énormément. Nous proposons donc d'utiliser les One-Class SVM, une technique qui dérive des SVM mais qui n'utilise que des exemples de pixels brûlés pour les phases d'apprentissage et de classification. Afin de prendre en compte l'information spatiale de l'image, l'algorithme OC-SVM est utilisé comme une technique de croissance de régions, ce qui permet de diminuer les fausses alarmes et d'améliorer les contours des zones brûlées. De plus, la base d'exemple de pixels brûlés nécessaire à l'apprentissage des techniques SVM est déterminée automatiquement à partir de l'histogramme de l'image. Finalement, la méthode de classification proposée est testée sur plusieurs images satellitaires afin de valider son efficacité selon le type de végétation et la surface des zones brûlées. Les zones brûlées obtenues sont comparées aux vérités de terrain fournies par le CNES, Infoterra France, le SERTIT, les Services Départementaux d'Incendies et de Secours ou l'Office National des Forêts.

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9 Conference articles

1 - Multi-spectral Image Analysis for Skin Pigmentation Classification. S. Prigent and X. Descombes and D. Zugaj and P. Martel and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Hong-Kong, China, September 2010. Keywords : skin hyper-pigmentation, Multi-spectral images, Support Vector Machines, Independant Component Analysis, Data reduction. @INPROCEEDINGS{sp02, author = {Prigent, S. and Descombes, X. and Zugaj, D. and Martel, P. and Zerubia, J.}, title = {Multi-spectral Image Analysis for Skin Pigmentation Classification}, year = {2010}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Hong-Kong, China}, pdf = {http://hal.inria.fr/docs/00/49/94/92/PDF/Article_ICIP.pdf}, keyword = {skin hyper-pigmentation, Multi-spectral images, Support Vector Machines, Independant Component Analysis, Data reduction} } Abstract : In this paper, we compare two different approaches for semi-automatic detection of skin hyper-pigmentation on multi-spectral images. These two methods are support vector machine (SVM) and blind source separation. To apply SVM, a dimension reduction method adapted to data classification is proposed. It allows to improve the quality of SVM classification as well as to have reasonable computation time. For the blind source separation approach we show that, using independent component analysis, it is possible to extract a relevant cartography of skin pigmentation.

2 - Spectral Analysis and Unsupervised SVM Classification for Skin Hyper-pigmentation Classification. S. Prigent and X. Descombes and D. Zugaj and J. Zerubia. In Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), Reykjavik, Iceland, June 2010. Keywords : Sectral analysis, Data reduction, Projection pursuit, Support Vector Machines, skin hyper-pigmentation. @INPROCEEDINGS{sp01, author = {Prigent, S. and Descombes, X. and Zugaj, D. and Zerubia, J.}, title = {Spectral Analysis and Unsupervised SVM Classification for Skin Hyper-pigmentation Classification}, year = {2010}, month = {June}, booktitle = {Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS)}, address = {Reykjavik, Iceland}, pdf = {http://hal.inria.fr/docs/00/49/55/60/PDF/whispers2010_submission_124.pdf}, keyword = {Sectral analysis, Data reduction, Projection pursuit, Support Vector Machines, skin hyper-pigmentation} } Abstract : Data reduction procedures and classification via support vector machines (SVMs) are often associated with multi or hyperspectral image analysis. In this paper, we propose an automatic method with these two schemes in order to perform a classification of skin hyper-pigmentation on multi-spectral images. We propose a spectral analysis method to partition the spectrum as a tool for data reduction, implemented by projection pursuit. Once the data is reduced, an SVM is used to differentiate the pathological from the healthy areas. As SVM is a supervised classification method, we propose a spatial criterion for spectral analysis in order to perform automatic learning.

3 - Multi-class SVM for forestry classification. N. Hajj Chehade and JG. Boureau and C. Vidal and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009. Keywords : Support Vector Machines, texture segmentation, Haralick feature, remote sensing, Forest vegetation. @INPROCEEDINGS{Nabil09, author = {Hajj Chehade, N. and Boureau, JG. and Vidal, C. and Zerubia, J.}, title = {Multi-class SVM for forestry classification}, year = {2009}, month = {November}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Cairo, Egypt}, url = {http://dx.doi.org/10.1109/ICIP.2009.5413395}, keyword = {Support Vector Machines, texture segmentation, Haralick feature, remote sensing, Forest vegetation} } Abstract : In this paper we propose a method for classifying the vegetation types in an aerial color infra-red (CIR) image. Different vegetation types do not only differ in color, but also in texture. We study the use of four Haralick features (energy, contrast, entropy, homogeneity) for texture analysis, and then perform the classification using the one-against-all (OAA) multi-class support vector machine (SVM), which is a popular supervised learning technique for classification. The choice of features (along with their corresponding parameters), the choice of the training set, and the choice of the SVM kernel highly affect the performance of the classification. The study was done on several CIR aerial images provided by the French National Forest Inventory (IFN). In this paper, we will show one example on a national forest near Sedan (in France), and compare our result with the IFN map.

4 - Unsupervised One-Class SVM Using a Watershed Algorithm and Hysteresis Thresholding to Detect Burnt Areas. O. Zammit and X. Descombes and J. Zerubia. In Proc. International Conference on Pattern Recognition and Image Analysis (PRIA), Nizhny Novgorod, Russia, September 2008. Keywords : Classification, Segmentation, Support Vector Machines, Burnt areas, Forest fires, Satellite images. Copyright : @INPROCEEDINGS{zammit_pria_08, author = {Zammit, O. and Descombes, X. and Zerubia, J.}, title = {Unsupervised One-Class SVM Using a Watershed Algorithm and Hysteresis Thresholding to Detect Burnt Areas}, year = {2008}, month = {September}, booktitle = {Proc. International Conference on Pattern Recognition and Image Analysis (PRIA)}, address = {Nizhny Novgorod, Russia}, pdf = {http://hal.inria.fr/inria-00316297/fr/}, keyword = {Classification, Segmentation, Support Vector Machines, Burnt areas, Forest fires, Satellite images} }

5 - Combining One-Class Support Vector Machines and hysteresis thresholding: application to burnt area mapping. O. Zammit and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, August 2008. Note : to appear. Keywords : Classification, Satellite images, Support Vector Machines, Burnt areas, Forest fires, Clustering. Copyright : @INPROCEEDINGS{zammit_eusipco_08, author = {Zammit, O. and Descombes, X. and Zerubia, J.}, title = {Combining One-Class Support Vector Machines and hysteresis thresholding: application to burnt area mapping}, year = {2008}, month = {August}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Lausanne, Switzerland}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7080254}, keyword = {Classification, Satellite images, Support Vector Machines, Burnt areas, Forest fires, Clustering} }

6 - Apprentissage non supervisé des SVM par un algorithme des K-moyennes entropique pour la détection de zones brûlées. O. Zammit and X. Descombes and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Troyes, France, September 2007. Keywords : Satellite images, Forest fires, Burnt areas, Classification, Support Vector Machines, Learning base. @INPROCEEDINGS{zammit_gretsi_07, author = {Zammit, O. and Descombes, X. and Zerubia, J.}, title = {Apprentissage non supervisé des SVM par un algorithme des K-moyennes entropique pour la détection de zones brûlées}, year = {2007}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Troyes, France}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_zammit_gretsi_07.pdf}, keyword = {Satellite images, Forest fires, Burnt areas, Classification, Support Vector Machines, Learning base} }

7 - Assessment of different classification algorithms for burnt land discrimination. O. Zammit and X. Descombes and J. Zerubia. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pages 3000-3003, Barcelone, Spain, July 2007. Keywords : Satellite images, Burnt areas, Support Vector Machines, Forest fires, Classification. Copyright : IEEE @INPROCEEDINGS{zammit_igarss_07, author = {Zammit, O. and Descombes, X. and Zerubia, J.}, title = {Assessment of different classification algorithms for burnt land discrimination}, year = {2007}, month = {July}, booktitle = {Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, pages = {3000-3003}, address = {Barcelone, Spain}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4423476}, keyword = {Satellite images, Burnt areas, Support Vector Machines, Forest fires, Classification} }

8 - Burnt area mapping using Support Vector Machines. O. Zammit and X. Descombes and J. Zerubia. In Proc. International Conference on Forest Fire Research, Figueira da Foz, Portugal, November 2006. Keywords : Satellite images, Forest fires, Burnt areas, Support Vector Machines. @INPROCEEDINGS{zammit_icffr_06, author = {Zammit, O. and Descombes, X. and Zerubia, J.}, title = {Burnt area mapping using Support Vector Machines}, year = {2006}, month = {November}, booktitle = {Proc. International Conference on Forest Fire Research}, address = {Figueira da Foz, Portugal}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_zammit_icffr_06.pdf}, keyword = {Satellite images, Forest fires, Burnt areas, Support Vector Machines} }

9 - Textural Kernel for SVM Classification in Remote Sensing : Application to Forest Fire Detection and Urban Area Extraction. F. Lafarge and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005. Keywords : Support Vector Machines, Learning base, Markov Fields, Forest fires, Urban areas. Copyright : IEEE @INPROCEEDINGS{lafarge_icip05, author = {Lafarge, F. and Descombes, X. and Zerubia, J.}, title = {Textural Kernel for SVM Classification in Remote Sensing : Application to Forest Fire Detection and Urban Area Extraction}, year = {2005}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Genoa, Italy}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_icip05.pdf}, keyword = {Support Vector Machines, Learning base, Markov Fields, Forest fires, Urban areas} }

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2 Technical and Research Reports

1 - Support Vector Machines for burnt area discrimination. O. Zammit and X. Descombes and J. Zerubia. Research Report 6343, INRIA, November 2007. Keywords : Forest fires, Burnt areas, Satellite images, Support Vector Machines, Classification. @TECHREPORT{zammit_RR_07, author = {Zammit, O. and Descombes, X. and Zerubia, J.}, title = {Support Vector Machines for burnt area discrimination}, year = {2007}, month = {November}, institution = {INRIA}, type = {Research Report}, number = {6343}, url = {http://hal.inria.fr/inria-00185101/fr/}, pdf = {http://hal.inria.fr/inria-00185101/fr/}, keyword = {Forest fires, Burnt areas, Satellite images, Support Vector Machines, Classification} } Résumé : Ce rapport aborde le problème de l'évaluation des dégâts après un feux de forêt. La détection est effectuée à partir d'une seule image satellite (SPOT 5) acquise après le feu. Afin de détecter les zones brûlées, nous utilisons une approche récente de classification nommée SVM (Séparateurs à Vaste Marge). Cette méthode est comparée aux algorithmes de classification plus conventionnels comme les K-moyennes ou les K-plus proches voisins, qui sont régulièrement utilisés en traitement d'image. Nous proposons également une méthode de classification non supervisée combinant les K-moyennes et les SVM. Les résultats fournis par les différentes techniques sont comparés à des vérités de terrain sur diverses zones brûlées. Abstract : This report addresses the problem of burnt area discrimination using remote sensing images. The detection is based on a single post-fire image acquired by SPOT 5 satellite. To delineate the burnt areas, we use a recent classification method called Support Vectors Machines (SVM). This approach is compared to more conventional classifiers such as K-means or K-nearest neighbours which are widely used in image processing. We also proposed a new automatic classification approach combining K-means and SVM. The results given by the different methods are finally compared to ground truths on various burnt areas

2 - Noyaux Texturaux pour les Problèmes de Classification par SVM en Télédétection. F. Lafarge and X. Descombes and J. Zerubia. Research Report 5370, INRIA, France, December 2004. Keywords : Support Vector Machines, Classification, Forest fires, Urban areas, Learning base, Markov Fields. @TECHREPORT{5370, author = {Lafarge, F. and Descombes, X. and Zerubia, J.}, title = {Noyaux Texturaux pour les Problèmes de Classification par SVM en Télédétection}, year = {2004}, month = {December}, institution = {INRIA}, type = {Research Report}, number = {5370}, address = {France}, url = {https://hal.inria.fr/inria-00070633}, pdf = {https://hal.inria.fr/file/index/docid/70633/filename/RR-5370.pdf}, ps = {https://hal.inria.fr/docs/00/07/06/33/PS/RR-5370.ps}, keyword = {Support Vector Machines, Classification, Forest fires, Urban areas, Learning base, Markov Fields} } Résumé : Nous détaillons dans ce rapport la construction de deux noyaux texturaux s'utilisant dans les problèmes de classification par «Support Vector Machines» en télédétection. Les SVM constituent une méthode de classification supervisée particulièrement bien adaptée pour traiter des données de grande dimension telles que les images satellitaires. Par cette méthode, nous souhaitons réaliser l'apprentissage de paramètres qui permettent la différenciation entre deux ensembles de pixels connexes non-identiques. Nous travaillons pour cela sur des fonctions noyaux, fonctions caractérisant une certaine similarité entre deux données. Dans notre cas, cette similarité sera fondée à la fois sur une notion radiométrique et sur une notion texturale. La principale difficulté rencontrée dans cette étude réside dans l'élaboration de paramètres texturaux pertinents qui modélisent au mieux l'homogénéité d'un ensemble de pixels connexes. Nous appliquons les noyaux proposés à deux problèmes de télédétection: la détection de feux de forêt et la détection de zones urbaines à partir d'images satellitaires haute résolusion. Abstract : We present in this report two textural kernels for «Support Vector Machines» classification applied to remote sensing problems. SVMs constitute a method of supervised classification well adapted to deal with data of high dimension, such as images. We would like to learn parameters which allow the differentiation between two sets of connected pixels. We also introduce kernel functions which characterize a notion of similarity between two pieces of data. In our case this similarity is based on a radiometric charateristic and a textural characteristic. The main difficulty is to elaborate textural parameters which are pertinent and characterize as well as possible the homogeneity of a set of connected pixels. We apply this method to remote sensing problems : the detection of forest fires and the extraction of urban areas in high resolution satellite images.

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