Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 552042
Title Change detection between digital surface models from airborne laser scanning and dense matching using convolutional neural networks
Author(s) Zhang, Z.; Vosselman, G.; Gerke, M.; Persello, C.; Tuia, D.; Yang, M.Y.
Source In: ISPRS Geospatial Week 2019, 10–14 June 2019, Enschede, The Netherlands. - ISPRS (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ) - p. 453 - 460.
Event 4th ISPRS Geospatial Week 2019, Enschede, 2019-06-10/2019-06-14
Department(s) PE&RC
Laboratory of Geo-information Science and Remote Sensing
Publication type Contribution in proceedings
Publication year 2019
Keyword(s) Airborne Laser Scanning - Change Detection - Convolutional Neural Network (CNN) - Dense Image Matching - Digital Surface Model (DSM)

Airborne photogrammetry and airborne laser scanning are two commonly used technologies used for topographical data acquisition at the city level. Change detection between airborne laser scanning data and photogrammetric data is challenging since the two point clouds show different characteristics. After comparing the two types of point clouds, this paper proposes a feed-forward Convolutional Neural Network (CNN) to detect building changes between them. The motivation from an application point of view is that the multimodal point clouds might be available for different epochs. Our method contains three steps: First, the point clouds and orthoimages are converted to raster images. Second, square patches are cropped from raster images and then fed into CNN for change detection. Finally, the original change map is post-processed with a simple connected component analysis. Experimental results show that the patch-based recall rate reaches 0.8146 and the precision rate reaches 0.7632. Object-based evaluation shows that 74 out of 86 building changes are correctly detected.

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