OBJECT-BASED FOREST CHANGE DETECTION USING HIGH RESOLUTION SATELLITE IMAGES

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.

• Publication year

  2013

• Venue

  ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

• Publication date

  2013-04-26

• Fields of study

  Geography, Environmental Science

• Identifiers
  DOI 10.5194/ISPRSARCHIVES-XXXVIII-3-W22-49-2011
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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