COMPARISON OF FILTERING ALGORITHMS USED FOR DTM PRODUCTION FROM AIRBORNE LIDAR DATA: A CASE STUDY IN BERGAMA, TURKEY

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Süleymanoğlu B., Soycan M.

GEODETSKI VESTNIK, cilt.63, sa.3, ss.395-413, 2019 (SSCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 63 Sayı: 3
  • Basım Tarihi: 2019
  • Doi Numarası: 10.15292/geodetski-vestnik.2019.03.395-414
  • Dergi Adı: GEODETSKI VESTNIK
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus
  • Sayfa Sayıları: ss.395-413
  • Anahtar Kelimeler: lidar, airborne lidar, filtering algorithm, digital elevation model, point cloud, BARE-EARTH EXTRACTION, MORPHOLOGICAL FILTER, ACCURACY ASSESSMENT, DEM GENERATION, ELEVATION, MODELS, AREAS, SLOPE
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

A light detection and ranging (lidar) system is one of the most important technologies used for generating digital terrain models (DTMs). The point cloud data obtained by these systems consist of data gathered from ground and nonground features. To create a DTM with high resolution and accuracy, ground and nonground data must be separated. Numerous filtering algorithms have been developed for this purpose. The aim of this study was testing the filtering performance of six different filtering algorithms in four different test areas with different land cover were selected that had topographical features and characteristics. The algorithms were adaptive triangulated irregular network (ATIN), elevation threshold with an expand window (ETEW), maximum local slope (MLS), progressive morphology (PM), iterative polynomial fitting (IPF), and multiscale curvature classification (MCC) algorithms. In the results, all the filters performed well on a smooth surface and produced more errors in complex urban areas and rough terrain with dense vegetation. The IPF filtering algorithm generated the best results for the first three test areas (smooth landscape, urban areas and agricultural areas), while ETEW performed best in the fourth test area (steep areas with dense vegetation and infrastructure).