Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 29-34, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-29-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 29-34, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-29-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

URBAN AREA CHANGE DETECTION USING TIME SERIES AERIAL IMAGES

C. Altuntas C. Altuntas
  • Selcuk University, Engineering Faculty, Department of Geomatics, 42075 Selcuklu Konya, Turkey

Keywords: Photogrammetry, Dense Point Cloud, Aerial Image, Historical Image, Urban Change Detection

Abstract. The urban area should be imaged in three-dimensional (3D) for planning, inspection and management. In addition fast urbanisation requires detection the urban area changes which have been occurred with new buildings, additional floor to current buildings and excavations. 3D surface model of urban area enables to extracting high information from them. On the other hand high density spatial data should be measured to creating 3D digital terrain surface model. The dense image matching method makes 3D measurement with high density from the images in a short time. The aim of this study is detection the urban area changes via comparison of time series point cloud data from historical stereoscopic aerial images. The changes were detected with the difference of these digital elevation models. The study area was selected from Konya city in Turkey, and it has a large number of new buildings and changes in topography. Dense point cloud data were created from historical aerial images belong to years of 1951, 1975 and 2010. Every threedimensional point cloud data were registered to global georeferenced coordinate system with ground control points created from the imaged objects such as building corner, fence, wall and etc. Then urban changes were detected with comparing the dense point cloud data by exploiting the iterative closest point (ICP) algorithm. Consequently, the urban changes were detected from point to surface distances between image based point clouds.