Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-3/W22, 49-54, 2011
https://doi.org/10.5194/isprsarchives-XXXVIII-3-W22-49-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
26 Apr 2013
OBJECT-BASED FOREST CHANGE DETECTION USING HIGH RESOLUTION SATELLITE IMAGES
N. Chehata1, C. Orny2,1, S. Boukir1, and D. Guyon2 1G&E Laboratory, ENSEGID, Bordeaux University, 1 Allée F. Daguin, 33607 Pessac Cedex, France
2EPHYSE Laboratory, INRA, 33140 Villenave d'Ornon, France
Keywords: Multitemporal classification, segmentation, feature selection, change detection, forest damage Abstract. 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.
Conference paper (PDF, 1987 KB)


Citation: Chehata, N., Orny, C., Boukir, S., and Guyon, D.: OBJECT-BASED FOREST CHANGE DETECTION USING HIGH RESOLUTION SATELLITE IMAGES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-3/W22, 49-54, https://doi.org/10.5194/isprsarchives-XXXVIII-3-W22-49-2011, 2011.

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