- Devis Tuia (1)
- G. Vosselman (1)
- George Vosselman (1)
- M.Y. Yang (1)
- Michael Ying Yang (1)
- Z. Zhang (1)
- Zhenchao Zhang (1)
Detecting Building Changes between Airborne Laser Scanning and Photogrammetric Data
Zhang, Zhenchao ; Vosselman, George ; Gerke, Markus ; Persello, Claudio ; Tuia, Devis ; Yang, Michael Ying - \ 2019
Remote Sensing 11 (2019)20. - ISSN 2072-4292 - 17 p.
Detecting topographic changes in an urban environment and keeping city-level point clouds up-to-date are important tasks for urban planning and monitoring. In practice, remote sensing data are often available only in different modalities for two epochs. Change detection between airborne laser scanning data and photogrammetric data is challenging due to the multi-modality of the input data and dense matching errors. This paper proposes a method to detect building changes between multimodal acquisitions. The multimodal inputs are converted and fed into a light-weighted pseudo-Siamese convolutional neural network (PSI-CNN) for change detection. Different network configurations and fusion strategies are compared. Our experiments on a large urban data set demonstrate the effectiveness of the proposed method. Our change map achieves a recall rate of 86.17%, a precision rate of 68.16%, and an F1-score of 76.13%. The comparison between Siamese architecture and feed-forward architecture brings many interesting findings and suggestions to the design of networks for multimodal data processing.
Change detection between digital surface models from airborne laser scanning and dense matching using convolutional neural networks
Zhang, Z. ; Vosselman, G. ; Gerke, M. ; Persello, C. ; Tuia, D. ; Yang, M.Y. - \ 2019
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.
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.