İHA Görüntülerinden Yol Yüzeyinin 3B Yoğun Rekonstrüksiyonu ve SfM Tabanlı Yazılımların Performansının Karşılaştırılması

Year 2021, Volume: 2 Issue: 2, 96 - 105, 29.09.2021

Mustafa Zeybek Serkan Biçici

https://doi.org/10.48123/rsgis.983251

Cited By: 3

Abstract

İnsansız Hava Aracı (İHA) teknolojisi, özellikle görüntü işlemede kullanılan en hızlı büyüyen teknolojilerden biridir. Yapıdan Hareket (SfM) tabanlı yazılımlar genellikle iki boyutlu İHA tabanlı görüntülerin üç boyutlu (3B) verilere dönüştürülmesinde kullanılır. Daha sonra binalar, ağaçlar ve yollar gibi nesneler daha fazla analiz için 3B verilerden sınıflandırılabilir. Bu çalışmada, 3 boyutlu verilerden yol yüzeyi değerlendirilmiştir. 3B verilerin doğruluğunu etkileyen birkaç faktör vardır. Bu çalışmada, İHA uçuş yüksekliği ve SfM tabanlı yazılım olmak üzere iki faktör değerlendirilmiştir. 35 metre ve 50 metre olmak üzere iki farklı uçuş irtifası kullanıldı. Alçak ve birbirine yakın uçuş irtifalarının sonuçlar üzerinde önemli bir fark yaratmadığı ve yakın sonuçlar ürettiği tespit edilmiştir. Diğer bir faktör ise farklı SfM tabanlı yazılımlardır. Bu çalışmada Agisoft Metashape ve Pix4D Mapper olmak üzere iki iyi bilinen SfM tabanlı yazılım kullanılmıştır. Bu çalışmada, Agisoft Metashape yazılımının Pix4D Mapper yazılımına göre daha doğru ve hızlı sonuçlar ürettiği tespit edilmiştir.

Keywords

İHA, 3B, Yol yüzeyi, SfM, Yazılım

3D Dense Reconstruction of Road Surface from UAV Images and Comparison of SfM Based Software Performance

Abstract

Unmanned Aerial Vehicle (UAV) technology is one of the fastest-growing technologies especially used in image processing. Structure-from-Motion (SfM) based software are usually used to convert two-dimensional UAV-based images into three-dimensional (3D) data. Then, objects such as buildings, trees, and roads can be classified from the 3D data for further analysis. In this study, the road surface generated from 3D data was evaluated. There are several factors that affect the accuracy of the 3D data. In this study, two factors, namely UAV flight altitude and SfM based software, were evaluated. Two different flight altitudes, which were 35 meters and 50 meters, were used. It was found that the lower flights with closer altitudes did not make a significant difference on the results and produced similar results. Another factor is different SfM based software. Two well-known SfM based software were used in this study, which were the Agisoft Metashape and Pix4D Mapper. In this case study, it was found that the Agisoft Metashape software produced more accurate and faster results than Pix4D Mapper software.

Keywords

UAV, 3D, Road surface, SfM, Software

References

• Barnhart, T., & Crosby, B. (2013). Comparing Two Methods of Surface Change Detection on an Evolving Thermokarst Using High-Temporal-Frequency Terrestrial Laser Scanning, Selawik River, Alaska. Remote Sensing, 5(6), 2813-2837.
• Biçici, S., & Zeybek, M. (2021). An approach for the automated extraction of road surface distress from a UAV-derived point cloud. Automation in Construction, 122, 103475. doi:10.1016/j.autcon.2020.103475
• Che, E., & Olsen, M. (2019). An Efficient Framework for Mobile Lidar Trajectory Reconstruction and Mo-norvana Segmentation. Remote Sensing, 11(7), 836. doi:10.3390/rs11070836
• DiFrancesco, P.-M., Bonneau, D., & Hutchinson, D. J. (2020). The Implications of M3C2 Projection Diameter on 3D Semi-Automated Rockfall Extraction from Sequential Terrestrial Laser Scanning Point Clouds. Remote Sensing, 12(11), 1885. doi:10.3390/rs12111885
• Elkhrachy, I. (2021). Accuracy Assessment of Low-Cost Unmanned Aerial Vehicle (UAV) Photogrammetry. Alexandria Engineering Journal, 60(6), 5579-5590.
• Fryskowska, A., & Wróblewski, P. (2018). Mobile Laser Scanning accuracy assessment for the purposeof base-map updating. Geodesy and Cartography, 67(1), 35-55.
• Girardeau-Montaut, D., Roux, M., Marc, R. e., l, & Thibault, G. (2005). Change detection on points cloud data acquired with a ground laser scanner. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(part 3), 30-35.
• Guan, H. Y., Li, J., Cao, S., & Yu, Y. T. (2016). Use of mobile LiDAR in road information inventory: a review. International Journal of Image and Data Fusion, 7(3), 219-242.
• Jiménez-Jiménez, S. I., Ojeda-Bustamante, W., Marcial-Pablo, M., & Enciso, J. (2021). Digital Terrain Models Generated with Low-Cost UAV Photogrammetry: Methodology and Accuracy. ISPRS International Journal of Geo-Information, 10(5), 285. doi:10.3390/ijgi10050285
• Lague, D., Brodu, N., & Leroux, J. (2013). Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z). ISPRS Journal of Photogrammetry and Remote Sensing, 82, 10-26.
• Li, X. q., Chen, Z. a., Zhang, L. t., & Jia, D. (2016). Construction and Accuracy Test of a 3D Model of Non-Metric Camera Images Using Agisoft PhotoScan. Procedia Environmental Sciences, 36, 184-190.
• Nex, F., & Remondino, F. (2013). UAV for 3D mapping applications: a review. Applied Geomatics, 6(1), 1-15. doi:10.1007/s12518-013-0120-x
• Ruzgiene, B., Aksamitauskas, C., Daugela, I., Prokopimas, S., Puodziukas, V., & Rekus, D. (2015). Uav Photogrammetry for Road Surface Modelling. Baltic Journal of Road and Bridge Engineering, 10(2), 151-158.
• Spreitzer, G., Tunnicliffe, J., & Friedrich, H. (2019). Using Structure from Motion photogrammetry to assess large wood (LW) accumulations in the field. Geomorphology, 346, 106851. doi:10.1016/j.geomorph.2019.106851
• Tepeköylü, S. (2016). Mobil Lidar Uygulamaları, Veri İşleme Yazılımları ve Modelleri. Geomatik, 1(1), 1-7. doi:10.29128/geomatik.294065
• Wang, J. A., Ma, H. T., Wang, C. M., & He, Y. J. (2018). Fast 3D reconstruction method based on UAV photography. Etri Journal, 40(6), 788-793.
• Zeybek, M. (2021). Accuracy assessment of direct georeferencing UAV images with onboard global navigation satellite system and comparison of CORS/RTK surveying methods. Measurement Science and Technology, 32(6), 065402. doi:10.1088/1361-6501/abf25d
• Zeybek, M., & Biçici, S. (2021). Geometric Feature Extraction of Road from UAV Based Point Cloud Data. In Ben Ahmed, M., Karaș, İ.R., Santos, D., Sergeyeva, O. & Boudhir, A.A. (Eds.), Innovations in Smart Cities Applications Volume 4 (pp. 435-449). Cham, Switzerland: Springer International Publishing.
• Zeybek, M., & Şanlıoğlu, İ. (2019). Point cloud filtering on UAV based point cloud. Measurement, 133, 99-111.