The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XXXVIII-3/W22
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
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

  26 Apr 2013

26 Apr 2013

OBJECT-BASED FOREST CHANGE DETECTION USING HIGH RESOLUTION SATELLITE IMAGES

N. Chehata1, C. Orny2,1, S. Boukir1, and D. Guyon2 N. Chehata et al.
  • 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.