Araştırma Makalesi
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LiDAR verilerinin CSF algoritmasıyla filtrelenmesi ve Sayısal Arazi Modeli üretimi

Yıl 2019, Cilt: 1 Sayı: 1, 21 - 25, 20.12.2019

Öz

Yükseklik bilgisi birçok mühendislik uygulaması için vazgeçilmez verilerdendir. Özellikle arazi yüzeylerini tanımlamakta yoğun olarak kullanılan ve Sayısal Arazi Modeli şeklinde tanımlanan topografik ürünler birçok mühendislik projesi için altlık olarak kullanılmaktadır. Son yıllarda LiDAR verileri ile geniş arazilere ait yüksek doğrulukta arazi modelleri üretimi yaygınlaşmıştır. Bu çalışmada LiDAR verisi CSF algoritması kullanılarak filtrelenmiş ve sayısal arazi modeli üretilmiştir. Üretilen arazi modeli referans veri ile hem nokta tabanlı hem de görüntü tabanlı karşılaştırılmıştır. Buna göre nokta tabanlı karşılaştırmada filtreleme işlemi % 85 üretici doğruluğuna sahiptir. Görüntü tabanlı karşılaştırma is referans arazi modeli ve üretilen arazi modellerinden elde edilmiştir. Bu iki arazi modeli arasındaki korelasyon yaklaşık %98 olarak hesaplanmıştır. Ayrıca yükseklik bilgisinin güvenilirliği için hesaplanan karesel ortalama hata 11 cm olarak bulunmuştur.

Kaynakça

  • Abo Akel, N., Zilberstein, O. and Doytsher, Y. (2003). Automatic DTM extraction from dense raw LiDAR data in urban areas. In FIG Working Week, Austria, 22 September.
  • Axelsson, P. (2000). DEM Generation from Laser Scanner Data Using adaptive TIN Models. In ISPRS Symposium, Amsterdam, 16 23 July, 110-117.
  • Awrangjeb, M., Lu, G. and Fraser, C. S. (2014). Automatic building extraction from LiDAR data covering complex urban scenes. In ISPRS Technical Commission III Symposium, Zurich, 5 – 7 September, 25-32.
  • Briese, C. (2010). Extraction of digital terrain models. In Vosselman, G. (Ed.), Airborne and terrestrial laser scanning, University of Technology, Vienna, 147-150.
  • Ding, M., Lyngbaek, K. and Zakhor, A. (2008). Automatic registration of aerial imagery with untextured 3D LiDAR models. In 26th IEEE Conference on Computer Vision and Pattern Recognition, Anchorage,USA, 23 June, 1-8.
  • Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80: 185-201.
  • Forlani, G. and Nardinocchi, C. (2007). Adaptive filtering of aerial laser scanning data. In ISPRS Symposium, Finland, 12 September, 130-35.
  • Gerke, M. and Xiao, J. (2014). Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification. ISPRS Journal of Photogrammetry and Remote Sensing, 87: 78-92.
  • Guan, H., Li, J. and Chapman, M.A. (2011). Urban thematic mapping by integrating LiDAR point cloud with colour imagery, GEOMATICA, 65(4): 375-385.
  • Guo, Q., Li, W., Yu, H., and Alvarez, O. (2010). Effects of topographic variability and LiDAR sampling density on several DEM interpolation methods. Photogrammetric Engineering and Remote Sensing, 76(6), 701-712.
  • Kraus, K. and Pfeifer, N. (1998). Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, 53: 193-203.
  • Lin, C. and Nevatia, R. (1998). Building detection and description from a single ıntensity ımage. Computer Vision and Image Understanding, 72: 101-121.
  • Mongus, D., Lukač, N., Obrul, D. and Žalik, B. (2013). Detection of planar points for building extraction from LiDAR data. In ISPRS Conference, Canada, 28 May, 21-26.
  • Moussa, A. and El-Sheimy, N. (2012). A new object based method for automated extraction of urban objects from airborne sensors data. ISPRS Congress, Melbourne, 25 August, 309-314
  • Müller, S. and Zaum, D. W. (2005). Robust buıldıng detectıon ın aerıal ımages. In Proc. ISPRS Workshop, Vienna, 29 Agust, 143-149.
  • Rutzinger, M., Rottensteiner, F., and Pfeifer, N. (2009). A Comparison of evaluation techniques for building extraction from airborne laser scanning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2: 11-20.
  • Shufelt, J. A. (1999). Performance evaluation and analysis of monocular building extraction from aerial imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21: 311-326.
  • Sithole, G. (2001). Filtering of laser altimetry data using a slope adaptive filter. In ISPRS Symposium, Annapolis, 24 October, 203-210
  • Sithole, G. and Vosselman, G. (2004). Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 59: 85-101.
  • Song, W. and Haithcoat, T. L (2005). Development of comprehensive accuracy assessment indexes for building footprint extraction. IEEE Transactions on Geoscience and Remote Sensing, 43: 402-404.
  • Vosselman, G. (2000). Slope based filtering of laser altimetry data. In ISPRS Symposium, Amsterdam, 22 July, 678-684.
  • Yang, B., Xu, W. and Dong, Z. (2013). Automated extraction of building outlines from airborne laser scanning point clouds. IEEE Geoscience and Remote Sensing Letters, 10: 1399-1403.
  • Zhang, K., Chen, S. C., Whitman, D., Shyu, M. L., Yan, J. and Zhang, C. (2003). A progressive morphological filter for removing nonground measurements from airborne LİDAR data. IEEE Transactions on Geoscience and Remote Sensing, 41: 872-882.
  • Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X.,- and Yan, G. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8: 501-511.
  • Zhou, Y., Zhao, H., Chen, M., Tu, J. and Yan, L. (2018). Automatic detection of lunar craters based on DEM data with the terrain analysis method. Planetary and Space Science. Corrected Proof, https://doi.org/10.1016/j.pss.2018.03.003.
Yıl 2019, Cilt: 1 Sayı: 1, 21 - 25, 20.12.2019

Öz

Kaynakça

  • Abo Akel, N., Zilberstein, O. and Doytsher, Y. (2003). Automatic DTM extraction from dense raw LiDAR data in urban areas. In FIG Working Week, Austria, 22 September.
  • Axelsson, P. (2000). DEM Generation from Laser Scanner Data Using adaptive TIN Models. In ISPRS Symposium, Amsterdam, 16 23 July, 110-117.
  • Awrangjeb, M., Lu, G. and Fraser, C. S. (2014). Automatic building extraction from LiDAR data covering complex urban scenes. In ISPRS Technical Commission III Symposium, Zurich, 5 – 7 September, 25-32.
  • Briese, C. (2010). Extraction of digital terrain models. In Vosselman, G. (Ed.), Airborne and terrestrial laser scanning, University of Technology, Vienna, 147-150.
  • Ding, M., Lyngbaek, K. and Zakhor, A. (2008). Automatic registration of aerial imagery with untextured 3D LiDAR models. In 26th IEEE Conference on Computer Vision and Pattern Recognition, Anchorage,USA, 23 June, 1-8.
  • Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80: 185-201.
  • Forlani, G. and Nardinocchi, C. (2007). Adaptive filtering of aerial laser scanning data. In ISPRS Symposium, Finland, 12 September, 130-35.
  • Gerke, M. and Xiao, J. (2014). Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification. ISPRS Journal of Photogrammetry and Remote Sensing, 87: 78-92.
  • Guan, H., Li, J. and Chapman, M.A. (2011). Urban thematic mapping by integrating LiDAR point cloud with colour imagery, GEOMATICA, 65(4): 375-385.
  • Guo, Q., Li, W., Yu, H., and Alvarez, O. (2010). Effects of topographic variability and LiDAR sampling density on several DEM interpolation methods. Photogrammetric Engineering and Remote Sensing, 76(6), 701-712.
  • Kraus, K. and Pfeifer, N. (1998). Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, 53: 193-203.
  • Lin, C. and Nevatia, R. (1998). Building detection and description from a single ıntensity ımage. Computer Vision and Image Understanding, 72: 101-121.
  • Mongus, D., Lukač, N., Obrul, D. and Žalik, B. (2013). Detection of planar points for building extraction from LiDAR data. In ISPRS Conference, Canada, 28 May, 21-26.
  • Moussa, A. and El-Sheimy, N. (2012). A new object based method for automated extraction of urban objects from airborne sensors data. ISPRS Congress, Melbourne, 25 August, 309-314
  • Müller, S. and Zaum, D. W. (2005). Robust buıldıng detectıon ın aerıal ımages. In Proc. ISPRS Workshop, Vienna, 29 Agust, 143-149.
  • Rutzinger, M., Rottensteiner, F., and Pfeifer, N. (2009). A Comparison of evaluation techniques for building extraction from airborne laser scanning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2: 11-20.
  • Shufelt, J. A. (1999). Performance evaluation and analysis of monocular building extraction from aerial imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21: 311-326.
  • Sithole, G. (2001). Filtering of laser altimetry data using a slope adaptive filter. In ISPRS Symposium, Annapolis, 24 October, 203-210
  • Sithole, G. and Vosselman, G. (2004). Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 59: 85-101.
  • Song, W. and Haithcoat, T. L (2005). Development of comprehensive accuracy assessment indexes for building footprint extraction. IEEE Transactions on Geoscience and Remote Sensing, 43: 402-404.
  • Vosselman, G. (2000). Slope based filtering of laser altimetry data. In ISPRS Symposium, Amsterdam, 22 July, 678-684.
  • Yang, B., Xu, W. and Dong, Z. (2013). Automated extraction of building outlines from airborne laser scanning point clouds. IEEE Geoscience and Remote Sensing Letters, 10: 1399-1403.
  • Zhang, K., Chen, S. C., Whitman, D., Shyu, M. L., Yan, J. and Zhang, C. (2003). A progressive morphological filter for removing nonground measurements from airborne LİDAR data. IEEE Transactions on Geoscience and Remote Sensing, 41: 872-882.
  • Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X.,- and Yan, G. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8: 501-511.
  • Zhou, Y., Zhao, H., Chen, M., Tu, J. and Yan, L. (2018). Automatic detection of lunar craters based on DEM data with the terrain analysis method. Planetary and Space Science. Corrected Proof, https://doi.org/10.1016/j.pss.2018.03.003.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Nizar Polat

Yayımlanma Tarihi 20 Aralık 2019
Gönderilme Tarihi 2 Aralık 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 1 Sayı: 1

Kaynak Göster

APA Polat, N. (2019). LiDAR verilerinin CSF algoritmasıyla filtrelenmesi ve Sayısal Arazi Modeli üretimi. Türkiye Lidar Dergisi, 1(1), 21-25.

Türkiye LiDAR Dergisi