The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1615–1620, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1615-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1615–1620, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1615-2020

  22 Aug 2020

22 Aug 2020

HIERARCHICAL APPROACH FOR DETECTING CHANGES WITH THE USE OF DIFFERENT PYRAMID LEVELS IN DENSE IMAGE MATCHING

M. Pilarska M. Pilarska
  • Faculty of Geodesy and Cartography, Warsaw University of Technology, Warsaw, Poland

Keywords: change detection, dense image matching, nadir imagery, image pyramid, buildings change detection

Abstract. Many cities order spatial data systematically, in particular aerial nadir images and orthophotomaps. However, only the orthoimages and orthophotomaps are usually used by the city administration, particularly in spatial planning. Some of the users are not aware of the possibilities as to how the aerial images can be used. Spatial data users, who may not be specialists in photogrammetry, are sometimes not aware that it is possible to obtain 3D information from 2D images as a point cloud. The idea of dense image matching (DIM) is well-known and described in the field of photogrammetry. Although dense image matching is a time- and memory-consuming process, this does not present a major drawback with modern computing. Images for the test area – Warsaw – are characterised by Ground Sampling Distance (GSD) equal to 8 cm. These images can be successfully used in change detection processes, comparing the dense image matching point cloud from two different dates. What is important while considering land cover change detection, is that it is not necessary to generate a detailed and high-density point cloud, e.g. in order to detect changes in buildings. The main idea of the article is to present the possibility of using higher levels of images pyramid in dense image matching within the change detection process as a way to optimize the processing time and point cloud accuracy. Which level of pyramid is needed to detect different changes in urban land cover will also be discussed.