Region-based automatic building and forest change detection on Cartosat-1 stereo imagery

Autor(en): Tian, J.
Reinartz, P.
d'Angelo, P.
Ehlers, M.
Stichwörter: AREA; Change detection; CLASSIFICATION; Digital Surface Model (DSM); Forest change; Geography, Physical; Geology; Geosciences, Multidisciplinary; Imaging Science & Photographic Technology; Industrial area change; LAND-COVER; Physical Geography; Remote Sensing; Stereo imagery
Erscheinungsdatum: 2013
Herausgeber: ELSEVIER
Journal: ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volumen: 79
Startseite: 226
Seitenende: 239
Zusammenfassung: 
In this paper a novel region-based method is proposed for change detection using space borne panchromatic Cartosat-1 stereo imagery. In the first step, Digital Surface Models (DSMs) from two dates are generated by semi-global matching. The geometric lateral resolution of the DSMs is 5 m x 5 m and the height accuracy is in the range of approximately 3 m (RMSE). In the second step, mean-shift segmentation is applied on the orthorectified images of two dates to obtain initial regions. A region intersection following a merging strategy is proposed to get minimum change regions and multi-level change vectors are extracted for these regions. Finally change detection is achieved by combining these features with weighted change vector analysis. The result evaluations demonstrate that the applied DSM generation method is well suited for Cartosat-1 imagery, and the extracted height values can largely improve the change detection accuracy, moreover it is shown that the proposed change detection method can be used robustly for both forest and industrial areas. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
ISSN: 09242716
DOI: 10.1016/j.isprsjprs.2013.02.017

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