Automatic Building Extraction from Image and LiDAR Data with Active Contour Segmentation
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Automatic building extraction is an important topic for many applications such as urban planning, disaster management, 3D building modeling and updating GIS databases. Its approaches mainly depend on two data sources: light detection and ranging (LiDAR) point cloud and aerial imagery both of which have advantages and disadvantages of their own. In this study, in order to benefit from the advantages of each data sources, LiDAR and image data combined together. And then, the building boundaries were extracted with the automated active contour algorithm implemented in MATLAB. Active contour algorithm uses initial contour positions to segment an object in the image. Initial contour positions were detected without user interaction by a series of image enhancements, band ratio and morphological operations. Four test areas with varying building and background levels of detail were selected from ISPRS’s benchmark Vaihingen and Istanbul datasets. Vegetation and shadows were removed from all the datasets by band ratio to improve segmentation quality. Subsequently, LiDAR point cloud data was converted to raster format and added to the aerial imagery as an extra band. Resulting merged image and initial contour positions were given to the active contour algorithm to extract building boundaries. In order to compare the contribution of LiDAR to the proposed method, the boundaries of the buildings were extracted from the input image before and after adding LiDAR data to the image as a layer. Finally extracted building boundaries were smoothed by the Awrangjeb (Int J Remote Sen 37(3): 551–579. https://doi.org/10.1080/01431161.2015.1131868, 2016) boundary regularization algorithm. Correctness (Corr), completeness (Comp) and accuracy (Q) metrics were used to assess accuracy of segmented building boundaries by comparing extracted building boundaries with manually digitized building boundaries. Proposed approach shows the promising results with over 93% correctness, 92% completeness and 89% quality.
KeywordsActive contour Automatic Building extraction High-resolution image LiDAR
The authors express their gratitude to German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) for ISPRS Vaihingen dataset. Also authors acknowledge The Istanbul Metropolitan Municipality for Istanbul datasets.
- Abdullah, S. M., Awrangjeb, M., & Lu, G. (2014). Automatic segmentation of LiDAR point cloud data at different height levels for 3D building extraction. In 2014 IEEE international conference on multimedia and expo workshops (ICMEW) (pp. 1–6). IEEE.Google Scholar
- Acar, H., Karsli, F., Ozturk, M., & Dihkan, M. (2018). Automatic detection of building roofs from point clouds produced by the dense image matching technique. International Journal of Remote Sensing, https://doi.org/10.1080/01431161.2018.1508915.
- Ahmadi, S., Zoej, M. J. V., Ebadi, H., Moghaddam, H. A., & Mohammadzadeh, A. (2010). Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours. International Journal of Applied Earth Observation and Geoinformation, 12(2010), 150–157.CrossRefGoogle Scholar
- Awrangjeb, M., Fraser, C. S., & Lua, G. (2013). Integration of LiDAR data and orthoimage for automatic 3D building roof plane extraction. In 2013 IEEE international conference on multimedia and expo (ICME) (pp. 1–6), IEEE.Google Scholar
- Chaudhuri, D., Kushwaha, N. K., Samal, A., & Agarwal, R. C. (2016). Automatic building detection from high-resolution satellite images based on morphology and internal gray variance. IEEE Journal of Selected Topıcs ın Applied Earth Observations and Remote Sensing, 9(5), 1767–1779.CrossRefGoogle Scholar
- Fazan, A. J. & Poz, A. P. D. (2013). Rectilinear building roof contour extraction based on snakes and dynamic programming. International Journal of Applied Earth Observation and Geoinformation, https://doi.org/10.1016/j.jag.2013.03.003.
- Ghaffarian, S. (2015). An approach for automatic building extraction from high resolution satellite images using shadow analysis and active contours model. Master Thesis, Hacettepe University, Ankara, Turkey.Google Scholar
- Karsli, F., Dihkan, M., Acar, H., & Ozturk, A. (2016). Automatic building extraction from very high-resolution image and LiDAR data with SVM algorithm. Arabian Journal of Geosciences, https://doi.org/10.1007/s12517-016-2664-7.
- Li, P., Jiang, S., Wang, X., & Zhang, J. 2013. Building extraction using lidar data and very high resolution image over complex urban area. In 2013 IEEE international geoscience and remote sensing symposium (IGARSS) (pp. 4253–4256), IEEE.Google Scholar
- Rottensteiner, F., Sohn, G., Gerke, M., & Wegner, J. D. (2013). ISPRS test project on urban classification and 3D building reconstruction. Commission III-Photogrammetric Computer Vision and Image Analysis, Working Group III/4-3D Scene Analysis, 1–17.Google Scholar
- URL-1. (2018). https://bimtas.istanbul. 14 Sept 2018.
- URL-2. (2018). https://www.mathworks.com/help/images/ref/activecontour.html. 22 May 2018.
- Varghese, V., Shajahan, D. A., & Nath, A. G. (2016). Building boundary tracing and regularization from LiDAR point cloud. In International conference on emerging technological trends (ICETT) (pp. 1–6), IEEE.Google Scholar
- Wang, Y. (2016). Automatic extraction of building outline from high resolution aerial imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B3, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic.Google Scholar
- Zhou, G., Member Senior, I. E. E. E., & Zhou, X. (2014). Seamless fusion of LiDAR and aerial imagery for building extraction. IEEE Transactions on Geoscience and Remote Sensing, 52(11), 7394–7407.Google Scholar