|
|
Publications about Forest fires
Result of the query in the list of publications :
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. |
|
 top of the page
8 Conference articles |
1 - 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} |
| } |
|
2 - 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} |
| } |
|
3 - Forest Fire Detection based on Gaussian field analysis.
F. Lafarge and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Poznan, Poland, September 2007.
Note : Copyright EURASIP
Keywords : Gaussian Field, DT-caracteristic, Forest fires.
@INPROCEEDINGS{lafarge_eusipco07,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Forest Fire Detection based on Gaussian field analysis}, |
| year |
= |
{2007}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Poznan, Poland}, |
| note |
= |
{Copyright EURASIP}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_lafarge_eusipco07.pdf}, |
| keyword |
= |
{Gaussian Field, DT-caracteristic, Forest fires} |
| } |
|
4 - 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} |
| } |
|
5 - 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
|
6 - 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} |
| } |
|
7 - Détection de feux de forêt à partir d'images satellitaires IRT par analyse statistique d'évènements rares.
F. Lafarge and X. Descombes and J. Zerubia and S. Mathieu-Marni. In Proc. GRETSI Symposium on Signal and Image Processing, Louvain-la-Neuve, Belgique, September 2005.
Keywords : Rare event, Forest fires, Gaussian Field.
@INPROCEEDINGS{lafarge_gretsi05,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Mathieu-Marni, S.}, |
| title |
= |
{Détection de feux de forêt à partir d'images satellitaires IRT par analyse statistique d'évènements rares}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Louvain-la-Neuve, Belgique}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_gretsi05.pdf}, |
| keyword |
= |
{Rare event, Forest fires, Gaussian Field} |
| } |
|
8 - 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} |
| } |
|
top of the page
3 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 |
|
top of the page
These pages were generated by 
|
New demo webpage
get the bibtex
