3D building change detection between current vhr images and past LiDAR data

K. Zhou*, B. Gorte, R. Lindenbergh, E. Widyaningrum

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

7 Citations (Scopus)
135 Downloads (Pure)


Change detection is an essential step to locate the area where an old model should be updated. With high density and accuracy, LiDAR data is often used to create a 3D city model. However, updating LiDAR data at state or nation level often takes years. Very high resolution (VHR) images with high updating rate is therefore an option for change detection. This paper provides a novel and efficient approach to derive pixel-based building change detection between past LiDAR and new VHR images. The proposed approach aims notably at reducing false alarms of changes near edges. For this purpose, LiDAR data is used to supervise the process of finding stereo pairs and derive the changes directly. This paper proposes to derive three possible heights (so three DSMs) by exploiting planar segments from LiDAR data. Near edges, the up to three possible heights are transformed into discrete disparities. A optimal disparity is selected from a reasonable and computational efficient range centered on them. If the optimal disparity is selected, but still the stereo pair found is wrong, a change has been found. A Markov random field (MRF) with built-in edge awareness from images is designed to find optimal disparity. By segmenting the pixels into plane and edge segments, the global optimization problem is split into many local ones which makes the optimization very efficient. Using an optimization and a consecutive occlusion consistency check, the changes are derived from stereo pairs having high color difference. The algorithm is tested to find changes in an urban areas in the city of Amersfoort, the Netherlands. The two different test cases show that the algorithm is indeed efficient. The optimized disparity images have sharp edges along those of images and false alarms of changes near or on edges and occlusions are largely reduced.

Original languageEnglish
Pages (from-to)1229-1235
Number of pages7
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Issue number2
Publication statusPublished - 30 May 2018
EventISPRS TC II Mid-term Symposium: Towards Photogrammetry 2020 - Riva del Garda, Italy
Duration: 4 Jun 20187 Jun 2018


  • Change detection
  • Disparity
  • DSM quality
  • LiDAR data
  • MRF
  • Optimization
  • VHR images


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