Textural kernel for SVM classification in remote sensing: Application to forest fire detection and urban extraction

Florent Lafarge 1 Xavier Descombes 1 Josiane Zerubia 1
1 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , SIS - Signal, Images et Systèmes
Abstract : We present a textural kernel 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 introduce kernel functions in order to favor the distiction between our class of interest and the other classes : it gives an information of similarity. In our case this similarity is based on radiometric and textural characteristics. One of the main difficulties is to elaborate textural parameters which are relevant and characterize as well as possible the joint distribution 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 images.
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Communication dans un congrès
ICIP - International Conference on Image Processing - 2005, Nov 2005, Genoa, Italy. IEEE, 3, pp.1096-1099, 2005, Image Processing, 2005. ICIP 2005. IEEE International Conference on. 〈10.1109/ICIP.2005.1530587〉
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Florent Lafarge, Xavier Descombes, Josiane Zerubia. Textural kernel for SVM classification in remote sensing: Application to forest fire detection and urban extraction. ICIP - International Conference on Image Processing - 2005, Nov 2005, Genoa, Italy. IEEE, 3, pp.1096-1099, 2005, Image Processing, 2005. ICIP 2005. IEEE International Conference on. 〈10.1109/ICIP.2005.1530587〉. 〈hal-00814966〉

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