Building and Tree Detection Using Multiscale Matched Filtering
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:79592", author = "Abdullah H. Özcan and Dilara Hisar and Yetkin Sayar and Cem Ünsalan", title = "Building and Tree Detection Using Multiscale Matched Filtering", 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.", volume = "14", number = "5", pages = "129-4", }
