An object-based approach for forest disaster change detection using High Resolution (HR) satellite images is proposed. An automatic feature selection process is used to optimize image segmentation via an original calibration-like procedure. A multitemporal classification then enables the separation of wind-fall from intact areas based on a new descriptor that depends on the level of fragmentation of the detected regions. The mean shift algorithm was used in both the segmentation and the classification processes. The method was tested on a high resolution Formosat-2 multispectral satellite image pair acquired before and after the Klaus storm. The obtained results are encouraging and the contribution of high resolution images for forest disaster mapping is discussed.

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mean shift algorithm method forest disaster change detection image segmentation intact high resolution formosat2 multispectral satellite image pair multitemporal classification objectbased approach calibrationlike regions automatic feature selection process windfall

D. Guyon,N. Chehata,C. Orny,S. Boukir,.OBJECT-BASED FOREST CHANGE DETECTION USING HIGH RESOLUTION SATELLITE IMAGES. XXXVIII-3/W22 (),.

D. Guyon,N. Chehata,C. Orny,S. Boukir,"OBJECT-BASED FOREST CHANGE DETECTION USING HIGH RESOLUTION SATELLITE IMAGES" XXXVIII-3/W22

D. Guyon,N. Chehata,C. Orny,S. Boukir,"OBJECT-BASED FOREST CHANGE DETECTION USING HIGH RESOLUTION SATELLITE IMAGES"