Extracting three-dimensional (3D) spatial information from sequential oblique unmanned aerial system (UAS) imagery for digital surface modeling

References

• Aicardi, I., F. Chiabrando, N. Grasso, A. M. Lingua, F. Noardo, and A. Spanó. 2016. “Uav Photogrammetry with Oblique Images: First Analysis on Data Acquisition and Processing.” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci XLI-B1: 835–842.
   Google Scholar
• Al-Rawabdeh, A., F. He, A. Moussa, N. El-Sheimy, and A. Habib. 2016. “Using an Unmanned Aerial Vehicle-based Digital Imaging System to Derive a 3d Point Cloud for Landslide Scarp Recognition.” Remote Sensing 8: 95.
   Web of Science ®Google Scholar
• Bay, H., T. Tuytelaars, and V. Gool. 2006. “Surf: Speeded up Robust Features.” Computer Vision – ECCV 2006 3951: 404–417.
   Google Scholar
• Beder, C., and R. Steffen. 2006. “Determining an Initial Image Pair for Fixing the Scale of a 3d Reconstruction from an Image Sequence.” Pattern Recognition 4174: 657–666.
   Web of Science ®Google Scholar
• Birchfield, S., and C. Tomasi. 1998. “A Pixel Dissimilarity Measure that Is Insensitive to Image Sampling.” IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (4): 401–406.
   Web of Science ®Google Scholar
• Börlin, N., A. Murtiyoso, P. Grussenmeyer, F. Menna, and E. Nocerino. 2019. “Flexible Photogrammetric Computations Using Modular Bundle Adjustment: The Chain Rule and the Collinearity Equations.” PE&RS 85 (5): 361–368 (8).
   Web of Science ®Google Scholar
• Bradski, G. 2000. “The Opencv Library”. Dr Dobb’s J. Software Tools, 25, 120-125
   Google Scholar
• Carrivick, J., M. Smith, and D. Quicey. 2016. Structure from Motion in Geosciences. Chichester, UK: Wiley.
   Google Scholar
• Cheng, M., and M. Matsuoka. 2020. “An Enhanced Image Matching Strategy Using Binary-stream Feature Descriptors.” IEEE Geoscience and Remote Sensing Letters 17 (7): 1253–1257.
   Web of Science ®Google Scholar
• Cho, W., T. Schenk, and M. Madani. 1993. “Resampling Digital Imagery to Epipolar Geometry.” The International Archives of the Photogrammetry, Remote Sensing 29: 404–408.
   Google Scholar
• Colomina, I., and P. Molina. 2014. “Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 92: 79–97.
   Web of Science ®Google Scholar
• Elnima, E. 2015. “A Solution for Exterior and Relative Orientation in Photogrammetry, A Genetic Evolution Approach.” Journal of King Saud University - Engineering Sciences 27 (1): 108–113.
   Google Scholar
• Fischler, M. 1980. “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography.” Communications of the ACM 1087: 381–395.
   Google Scholar
• Fraser, C. 2018. Sfm and Photogrammetry: What’s in a Name? In Proceedings of the ISPRS Technical Comission II: Symposium 2018 “Towards Photogrammetry 2020”, Riva del Garda, Italy, 3–7 June.Riva del Garda, Italy.
   Google Scholar
• Furukawa, Y., and J. Ponce. 2010. “Accurate, Dense, and Robust Multiview Stereopsis.” IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (8): 1362–1376.
   PubMed Web of Science ®Google Scholar
• Gabrlik, P. 2015. “The Use of Direct Georeferencing in Aerial Photogrammetry with Micro Uav.” IFAC-PapersOnLine 48 (4): 380–385. 13th IFAC and IEEE Conference on Programmable Devices and Embedded Systems.
   Google Scholar
• Granshaw, S. 2018. “Structure from Motion: Origins and Originality.” The Photogrammetry Record 33: 6–10.
   Web of Science ®Google Scholar
• Granshaw, S., and C. Fraser. 2015. “Computer Vision and Photogrammetry: Interaction or Introspection?” The Photogrammetric Record 30 (149): 3–7.
   Web of Science ®Google Scholar
• Grün, A. 1982. “The Accuracy Potential of the Modern Bundle Block Adjustment in Aerial Photogrammetry.” PE&RS 48 (1): 45–54.
   Web of Science ®Google Scholar
• Habib, A. F., and A. M. Pullivelli 2005. Camera Stability Analysis and Geo-referencing. Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS ’05., 1169–1172.Seoul, South Korea.
   Google Scholar
• Hartley, R., and J. Mundy 1993. Relationship between Photogrammmetry and Computer Vision. Proc. SPIE 1944, Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision, 1944.Orlando, FL, United State.
   Google Scholar
• Hartly, R. 1995. In Defence of the 8-point Algorithm. Proc. 5th International Conference on Computer Vision, 1064–1070.Cambridge, Massachusetts: Massachusetts Institute of Technology.
   Google Scholar
• Hartly, R., and A. Zisserman. 2004. Multiple View Geometry in Computer Vision -. 2nd ed. Cambridge University Press.
   Google Scholar
• Harwin, S., and A. Lucieer. 2012. “Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-view Stereopsis from Unmanned Aerial Vehicle (Uav) Imagery.” Remote Sensing 4 (6): 1573–1599.
   Web of Science ®Google Scholar
• He, F., and A. Habib. 2014. “Linear Approach for Initial Recovery of the Exterior Orientation Parameters of Randomly Captured Images by Low-cost Mobile Mapping Systems.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1: 149–154.
   Google Scholar
• Hess-Flores, M., S. Recker, and K. I. Joy 2014. Uncertainty, Baseline, and Noise Analysis for L1 Error-based Multi-view Triangulation. 2014 22nd International Conference on Pattern Recognition, 4074–4079. Stockholm, Sweden.
   Google Scholar
• Hirschmuller, H. 2005. Accurate and Efficient Stereo Processing by Semi-global Matching and Mutual Information. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2, 807–814. San Diego, California.
   Google Scholar
• Hirschmuller, H. 2007. “Stereo Processing by Semiglobal Matching and Mutual Information.” IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (2): 328–341.
   Web of Science ®Google Scholar
• Hirschmuller, H. 2011. “Semi-global Matching - Motivation, Developments and Applications.” Photogrammetric Week 11: 173–184.
   Google Scholar
• Huang, P., K. Matzen, J. Kopf, N. Ahuja, and J. Huang 2018. Deepmvs: Learning Multiview Stereopsis. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’18), 2821–2830. Salt Lake City, UT, USA.
   Google Scholar
• Jiang, S., and W. Jiang. 2018. “Efficient Sfm for Oblique Uav Images: From Match Pair Selection to Geometrical Verification.” Remote Sensing 8: 10.
   Google Scholar
• Jiang, S., and W. Jiang. 2020. “Efficient Match Pair Selection for Oblique Uav Images Based on Adaptive Vocabulary Tree.” ISPRS Journal of Photogrammetry and Remote Sensing 161: 61–75.
   Web of Science ®Google Scholar
• Kanatani, K., and Y. Sugaya. 2011. Bundle Adjustment for 3-d Reconstruction: Implementation and Evaluation, 45. Okayama University.
   Google Scholar
• Kim, T., and J. I.and Kim. 2016. “Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence.” Sensors 16 (3): 412.
   PubMed Web of Science ®Google Scholar
• Kyung, M.-H., M.-S. Kim, and S. Hong. 1996. “A New Approach to Through-the-lens Camera Control.” Graphical Models and Image Processing 58 (3): 262–285.
   Google Scholar
• Lepetit, V., F. Moreno-Noguer, and P. Fua. 2009. “Epnp: An Accurate O(n) Solution to the Pnp Problem.” International Journal of Computer Vision 81: 155.
   Web of Science ®Google Scholar
• Leutenegger, S., and M. Chli2011. Brisk: Binary Robust Invariant Scalable Keypoints. 2011 International Conference on Computer Vision (ICCV’11), 2548–2555. Barcelona, Spain.
   Google Scholar
• Li, J., E. Li, Y. Chen, L. Xu, and Y. Zhang 2010. Bundled Depth-map Merging for Multiview Stereo. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’10), 2769–2776. San Francisco, CA, USA.
   Google Scholar
• Liu, J., L. Zhang, Z. Wang, and R. Wang. 2020. “Dense Stereo Matching Strategy for Oblique Images that Considers the Plane Directions in Urban Areas.” IEEE Transactions on Geoscience and Remote Sensing 58 (7): 5109–5116.
   Web of Science ®Google Scholar
• Loop, C., and Z. Zhang 1999. Computing Rectifying Homographies for Stereo Vision. Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 1, 125–131. Fort Collins, CO, USA.
   Google Scholar
• Lowe, D. G. 2004. “Distinctive Image Features from Scale-invariant Keypoints.” International Journal of Computer Vision 60 (2): 91–110.
   Web of Science ®Google Scholar
• Lu, X. 2018. “A Review of Solutions for Perspective-n-point Problem in Camera Pose Estimation.” Journal of Physics 1087: 052009.
   Google Scholar
• Luhmann, T., C. Fraser, and H. Maas. 2016. “Sensor Modelling and Camera Calibration for Close-range Photogrammetry.” ISPRS Journal of Photogrammetry and Remote Sensing 115: 37–46.
   Web of Science ®Google Scholar
• Luhmann, T., R. Stuart, K. Stephen, and B. Jan. 2013. Close-range Photogrammetry and 3d Imaging -. 3rd ed. De Gruyter. Berlin, Germany.
   Google Scholar
• Mahler, C. F., E. Varanda, and L. C. D. de Oliveira. 2012. “Analytical Model of Landslide Risk Using Gis.” Open Journal of Geology 2: 3.
   Google Scholar
• Mian, O., J. Lutes, G. Lipa, J. J. Hutton, E. Gavelle, and S. Borghini. 2015. “Direct Georeferencing on Small Unmanned Aerial Platforms for Improved Reliability and Accuracy of Mapping without the Need for Ground Control Points.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4: 397–402. XL-1/W4.
   Google Scholar
• Michelini, M., and H. Mayer. 2020. “Structure from Motion for Complex Image Sets.” ISPRS Journal of Photogrammetry and Remote Sensing 166: 140–152.
   Web of Science ®Google Scholar
• Nex, F., E. Rupnik, and F. Remondino. 2013. “Building Footprints Extraction from Oblique Imagery.” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci: 61–66. II-3/W3.
   Google Scholar
• Nister, D. 2004. “An Efficient Solution to the Five-point Relative Pose Problem.” IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (6): 756–770.
   PubMed Web of Science ®Google Scholar
• Pan, Y., Y. Dong, D. Wang, A. Chen, and Z. Ye. 2019. “Three-dimensional Reconstruction of Structural Surface Model of Heritage Bridges Using Uav-based Photogrammetric Point Clouds.” Remote Sensing 11: 1024.
   Web of Science ®Google Scholar
• Pierrot-Deseilligny, M., and N. Paparoditis. 2006.“A Multiresolution and Optimization-based Image Matching Approach: An Application to Surface Reconstruction from Spot5-hrs Stereo Imagery.” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 36: 1/W41.
   Google Scholar
• Qin, R. 2014. “An Object-based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images.” Remote Sensing 6: 7911–7932.
   Web of Science ®Google Scholar
• Rothermel, M. 2017. Development of a sgm-based multi-view reconstruction framework for aerial imagery ( Unpublished doctoral dissertation). University of Stuttgart.
   Google Scholar
• Rothermel, M., K. Gong, D. Fritsch, K. Schindler, and N. Haala. 2020. “Photometric Multiview Mesh Refinement for High-resolution Satellite Images.” ISPRS Journal of Photogrammetry and Remote Sensing 166: 52–62.
   Web of Science ®Google Scholar
• Rublee, E., V. Rabaud, K. Konolige, and G. Bradski 2011. Orb: An Efficient Alternative to Sift or Surf. 2011 International Conference on Computer Vision (ICCV’11), 2564–2571. Barcelona, Spain.
   Google Scholar
• Rupnik, E., F. Nex, and F. Remondino 2014. “Oblique Multi-camera Systems – Orientation and Dense Matching Issues”. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W1, 107–114.
   Google Scholar
• Seitz, S., B. Curless, J. Diebel, D. Scharstein, and R. Szeliski 2006. A Comparison and Evaluation of Multi-view Stereo Reconstruction Algorithms. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), 1, 519–528.New York, NY.
   Google Scholar
• Shen, S. 2013. “Accurate Multiple View 3d Reconstruction Using Patch-based Stereo for Largescale Scenes.” IEEE Transactions on Image Processing 22 (5): 1901–1914.
   PubMed Web of Science ®Google Scholar
• Siebert, S., and J. Teizer. 2014. “Mobile 3d Mapping for Surveying Earthwork Projects Using an Unmanned Aerial Vehicle (Uav) System.” Automation in Construction 41: 1–14.
   Web of Science ®Google Scholar
• Strutz, T. 2016. Data Fitting and Uncertainty - a Practical Introduction to Weighted Least Squares and beyond -. 2nd ed. Germany: Vieweg+Teubner, Wiesbaden.
   Google Scholar
• Szeliski, R. 2010. Computer Vision: Algorithms and Applications. New York, USA: Springer.
   Google Scholar
• Tanathong, S., and I. Lee. 2010. “Speeding up the Klt Tracker for Real-time Image Georeferencing Using Gps/ins Data.” Korean Journal of Remote Sensing 26: 629–644.
   Google Scholar
• Toschi, I., E. Nocerino, F. Remondino, A. Revolti, G. Soria, and S. Piffer. 2017. “Geospatial Data Processing for 3d City Model Generation, Management and Visualization.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1: 527–534..
   Google Scholar
• Tran, T. 2009. “Epipolar Resampling of Stereo Image Base on Airbase in the Digital Photogrammetry”. 7th FIG Regional Conference. Hanoi, Vietnam.
   Google Scholar
• Triggs, B., P. McLauchlan, R. Hartley, and A. Fitzgibbon 2000. “Bundle Adjustment – A Modern Synthesis”. Vision algorithms: theory and practice, lncs, 298–375.
   Google Scholar
• Tsai, C., and Y. Lin. 2017. “An Accelerated Image Matching Technique for Uav Orthoimage Registration.” ISPRS Journal of Photogrammetry and Remote Sensing 128: 130–145.
   Web of Science ®Google Scholar
• Tumurbaatar, T., and T. Kim 2018. Comparision of Computer Vision and Photogrammetric Approaches for Motion Estimation of Object in an Image Sequence. 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), 581–585. Chongqing, China.
   Google Scholar
• Verykokou, S., and C. Ioannidis 2018. “A Photogrammetry-based Structure from Motion Algorithm Using Robust Iterative Bundle Adjustment Techniques”. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W6, 73–80.
   Google Scholar
• Westoby, M., J. Brasington, N. Glassera, M. Hambrey, and J. Reynolds. 2012. “Structure- From-motion’ Photogrammetry: A Low-cost, Effective Tool for Geoscience Applications.” Geomorphology 179: 300–314.
   Web of Science ®Google Scholar
• Wojciechowska, G., and J. Łuczak 2018. “Use of Close-range Photogrammetry and Uav in Documentation of Architecture Monuments”. E3S Web Conf. XVIII Conference of PhD Students and Young Scientists “Interdisciplinary Topics in Mining and Geology, 71.
   Google Scholar
• Wolf, P., B. Dewitt, and B. Wilkinson. 2014. Elements of Photogrammetry with Applications in Gis -. 4th ed. New York, NY, USA: McGraw-Hill Science.
   Google Scholar
• Zhang, J. 2013. “Dense Point Could Extraction from Oblique Imagery”. (Unpublished master’s thesis). Rochester Institute of Technology.
   Google Scholar
• Zhang, J., M. Boutin, and D. Aliaga 2006. “Robust Bundle Adjustment for Structure from Motion”. 2006 International Conference on Image Processing, 2185–2188. Atlanta, GA, USA.
   Google Scholar