Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32759
Building and Tree Detection Using Multiscale Matched Filtering

Authors: Abdullah H. Özcan, Dilara Hisar, Yetkin Sayar, Cem Ünsalan

Abstract:

In this study, an automated building and tree detection method is proposed using DSM data and true orthophoto image. A multiscale matched filtering is used on DSM data. Therefore, first watershed transform is applied. Then, Otsu’s thresholding method is used as an adaptive threshold to segment each watershed region. Detected objects are masked with NDVI to separate buildings and trees. The proposed method is able to detect buildings and trees without entering any elevation threshold. We tested our method on ISPRS semantic labeling dataset and obtained promising results.

Keywords: Building detection, tree detection, matched filtering, multiscale, local maximum filtering, watershed segmentation.

Digital Object Identifier (DOI): doi.org/1

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 481

References:


[1] T. Brandtberg and F. Walter, “An algorithm for delineation of individual tree crowns in high spatial resolution aerial images using curved edge segments at multiple scales,” Proceedings of Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, pp. 41–54, 1998.
[2] ——, “Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis,” Machine Vision and Applications, vol. 11, no. 2, pp. 64–73, 1998.
[3] D. S. Culvenor, “Development of a tree delineation algorithm for application to high spatial resolution digital imagery of australian native forest,” 2000.
[4] ——, “TIDA an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery,” Computers & Geosciences, vol. 28, no. 1, pp. 33–44, 2002.
[5] M. Larsen, “Crown modelling to find tree top positions in aerial photographs,” in Third International Airborne Remote Sensing Conference and Exhibition, vol. 7, 1997, p. 10.
[6] D. Mongus, N. Lukac, and B. Zalik, “Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 93, pp. 145–156, 2014.
[7] D. Mongus and B. Zalik, “Parameter-free ground filtering of LiDAR data for automatic DTM generation,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 67, pp. 1–12, 2012.
[8] N. Otsu, “A threshold selection method from gray-level histograms,” Automatica, vol. 11, no. 285-296, pp. 23–27, 1975.
[9] A. H. Özcan, C. Ünsalan, and P. Reinartz, “Building detection using local features and DSM data,” in Proceedings of RAST’13, 2013, pp. 139–143.
[10] A. H. Ozcan, Y. Sayar, D. Hisar, and C. Unsalan, “Multiscale tree analysis from satellite images,” in Recent Advances in Space Technologies (RAST), 2015 7th International Conference on. IEEE, 2015, pp. 265–269.
[11] T. J. Pingel, K. C. Clarke, and W. A. McBride, “An improved simple morphological filter for the terrain classification of airborne LIDAR data,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 77, pp. 21–30, 2013.
[12] J. Pitkänen, “Individual tree detection in digital aerial images by combining locally adaptive binarization and local maxima methods,” Canadian Journal of Forest Research, vol. 31, no. 5, pp. 832–844, 2001.
[13] D. Pouliot and D. King, “Approaches for optimal automated individual tree crown detection in regenerating coniferous forests,” Canadian Journal of Remote Sensing, vol. 31, no. 3, pp. 255–267, 2005.
[14] D. A. Pouliot, D. J. King, and D. G. Pitt, “Development and evaluation of an automated tree detection delineation algorithm for monitoring regenerating coniferous forests,” Canadian Journal of Forest Research, vol. 35, no. 10, pp. 2332–2345, 2005.
[15] L. J. Quackenbush, P. F. Hopkins, and G. J. Kinn, “Using template correlation to identify individual trees in high resolution imagery,” in ASPRS Annual Conference, 2000.
[16] C. Ünsalan and K. L. Boyer, “Linearized vegetation indices based on a formal statistical framework,” IEEE Transactions on Geoscience and Remote Sensing, vol. 42, pp. 1575–1585, 2004.
[17] N. A. Walsworth and D. J. King, “Comparison of two tree apex delineation techniques,” in Proc. of the International Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, 1998, pp. 93–104.
[18] L. Wang, P. Gong, and G. S. Biging, “Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery,” Photogrammetric Engineering & Remote Sensing, vol. 70, no. 3, pp. 351–357, 2004.
[19] M. Wulder, K. O. Niemann, and D. G. Goodenough, “Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery,” Remote Sensing of Environment, vol. 73, no. 1, pp. 103–114, 2000.
[20] K. Zhang, S. C. Chen, D. Whitman, M. L. Shyu, J. Yan, and C. Zhang, “A progressive morphological filter for removing nonground measurements from airborne LIDAR data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 4, pp. 872–882, 2003.