Photogrammetric 3D Modelling Potential Comparison of SFM-Based New Generation Image Matching Software

Öz

Structure from motion (SFM) matching algorithm is the basic principle of new generation and widely used image matching software. Although these software work in common principle, their final products may contain different characteristics and distortions depending on their buried parameters. In the literature, there is lack of publishments which compare the three dimensional modelling performance of SFM based new generation software. Accordingly, our research group decided to carry out a study that could be a reference for upcoming researches. In this study, using VisualSFM, Agisoft and Pix4D SFM based image matching software, 3D digital surface models (DSM) were generated from unmanned air vehicle (UAV) high resolution aerial photos in a Campus of Zonguldak Bulent Ecevit University. Generated DSMs were comprehensively evaluated and compared by visual and statistical approaches utilizing the Agisoft DSM as the reference. The results clearly demonstrated the pros and cons of each analyzed SFM-based software.

Anahtar Kelimeler

• SFM
• VisualSFM
• Agisoft
• Pix4D
• Digital Surface Model

SFM Tabanlı Yeni Nesil Görüntü Eşleştirme Yazılımlarının Fotogrametrik 3B Modelleme Potansiyellerinin Karşılaştırması

Öz

Lokal hareket işaretleri ile iki boyutlu görüntü dizilerinin birleşiminden üç boyutlu (3B) yapıları kestirebilmek için geliştirilmiş Hareketten Yapı (Structure From Motion, SFM) fotogrametrik görüntü eşleştirme algoritması, yeni nesil ve yaygın olarak kullanılan bulut tabanlı görüntü eşleştirme yazılımlarının temel prensibidir. Bu yazılımlar ortak prensipte çalışmasına rağmen, kullanıcı tarafından müdahele edilemeyen gömülü parametrelerine bağlı olarak 3B sonuç ürünleri farklı özellikler ve distorsiyonlar içermektedir. Bu çalışmada, Zonguldak Bülent Ecevit Üniversitesi Çaycuma Kampüsü'nde insansız hava aracı (İHA) ile elde edilen yüksek çözünürlüklü hava fotoğraflarından VisualSFM, Agisoft ve Pix4D SFM tabanlı yeni nesil görüntü eşleme yazılımları kullanılarak eş grid aralıklı 3B dijital yüzey modelleri (DYM) üretilmiştir. Üretilen DYM'ler kapsamlı bir şekilde değerlendirilmiş ve Agisoft DYM'si referans olarak kullanılarak DYM’ler görsel ve istatistiksel yaklaşımlarla karşılaştırılmıştır. Standart sapma ve normalize medyan mutlak sapma temelinde elde edilen sonuçlar, analiz edilen SFM tabanlı yazılımların artılarını ve eksilerini açıkça ortaya koymuştur.

Anahtar Kelimeler

• SFM
• VisualSFM
• Agisoft
• Pix4D
• Dijital Yüzey Modeli

Kaynakça

1. Alidoost, F. and H. Arefi. 2017. Comparıson Of Uas-Based Photogrammetry Software For 3d Poınt Cloud Generatıon: A Survey Over A Hıstorıcal Sıte. Pp. 55–61 in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. 4. Copernicus GmbH.
2. Alobeid, A., Jacobsen, K., Heipke, C., 2010. Comparison of Matching Algorithms for DSM generation in urban areas from IKONOS imagery. Photogrammetric Engineering & Remote Sensing, 76(9):1041–1050.
3. Baltsavias, E., Gruen, A., Eisenbeiss, H., Zhang, L., Waser, T., 2008. High-quality image matching and automated generation of 3D tree models. International Journal of Remote Sensing 29(5):1243–1259.
4. Birdal, A. C., Avdan, U., & Türk, T. (2017). Estimating tree heights with images from an unmanned aerial vehicle. Geomatics, Natural Hazards and Risk,8(2),1144–1156. 10.1080/19475705.2017.1300608
5. Carrivick, J. L., Smith, M. W., Quincey, D. J. (2016). Structure from Motion in the Geosciences. Wiley-Blackwell, 208 sayfa. ISBN 978-1-118-89584-9
6. Comert, R., Avdan, U., Gorum, T., & Nefeslioglu, H. A. (2019). Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data. Engineering Geology, 260(February), 105264/j.enggeo.2019.105264
7. Jiang, S., & Jiang, W. (2018). Efficient SfM for Oblique UAV Images: From Match Pair Selection to Geometrical Verification. Remote Sensing, 10(8), 1246. 10.3390/rs10081246
8. Hartley, R. ve Zisserman, A. (2003). Multiple View Geometry in Computer Vision. Cambridge University Press. ISBN 978-0-521-54051-3.

9. Hellerstein, J. M., 2008. Quantitative Data Cleaning for Large Databases. Technical Report Presented at United Nations Economic Commission for Europe (UNECE), p. 42.
10. Rosnell, T., Honkavaara, E., 2012. Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera. Sensors 12:453–480.
11. Swatantran, A., Tang, H., Barrett, T., 2016. Rapid, high-resolution forest structure and terrain mapping over large areas using single photon lidar. Sci Rep 6:1–12.
12. Teizer, J., Kim, C., Bosché, F., 2005. Real-time 3D Modelling for Accelerated and Safer Construction using Emerging Technology. 539–543
13. Yang, Ying, Zongjian Lin, and Fengzhu Liu. 2016. Stable Imaging and Accuracy Issues of Low-Altitude Unmanned Aerial Vehicle Photogrammetry Systems. Remote Sensing 8(4):316.
14. Zongjian, Lin, Su Guozhong, and Xie Feifei. 2012. Uav-Borne Low Altıtude Photogrammetry System. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B1. Melbourne (Australia).