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Bir Fotogrametrik Bilgisayarlı Görü Tekniği olarak İHA Fotogrametrisi

Year 2023, Volume: 5 Issue: 2, 59 - 71, 31.12.2023

Ozan Arslan

https://doi.org/10.51534/tiha.1392600

Abstract

Geleneksel fotogrametriden farklı olarak, düşük maliyetli metrik olmayan dijital kameralarla farklı yüksekliklerden ve farklı açılardan çekilmiş görüntü verilerinin toplanmasına olanak sağlayan İHA-fotogrametrisi, bilgisayarlı görü ve fotogrametrinin kombinasyonunu içeren yöntemler ve iş akış sürecine sahiptir. Bu kapsamda kullanılmakta olan Hareket Tabanlı Yapısal Algılama (SfM) tekniği, İHA- tabanlı ortofoto ve 3B arazi modeli üretiminin standart tekniği haline gelmiştir. Bu nedenle literatürde SFM fotogrametrisi terminolojisi de kullanılmaya başlamıştır. Bu çalışmada birbirine yakın terminolojiye sahip ve iç içe geçmiş yöntem ve algoritmalara sahip bu teknikler (fotogrametri, İHA-fotogrametrisi, SfM fotogrametrisi) arasındaki ayrım ve benzerlikler kavramsal olarak ve bir uygulama pratiği açısından analiz edilecektir.

Keywords

İHA- fotogrametrisi SfM çoklu- görüntü stereo SIFT demet dengelemesi

Thanks

Bu çalışmada kullanılan verilerin temininde destek olan KOÜ Harita Müh. Bölümü öğrencileri Hasan Bütüner ve Furkan Kaya ile EFA Harita Müh. Prj. İnş. Taah. Gyr. Değ. Dnş. Ltd. Şti.’ ne teşekkürlerimi sunarım.

References

  • Agüera-Vega, F., Carvajal-Ramírez, F., & Martínez-Carricondo, P. (2017). Accuracy of digital surface models and orthophotos derived from unmanned aerial vehicle photogrammetry. Journal of Surveying Engineering, 143(2), 04016025.
  • Amrullah, C., Suwardhi, D., & Meilano, I. (2016). Product accuracy effect of oblique and vertical non-metric digital camera utilization in UAV-photogrammetry to determine fault plane. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 41-48.
  • Balázsik, V., Tóth, Z., & Abdurahmanov, I. (2021). Analysis of Data Acquisition Accuracy with UAV. Int. J. Geoinf, 17, 1-10.
  • Grayson, B., Penna, N. T., Mills, J. P., & Grant, D. S. (2018). GPS precise point positioning for UAV photogrammetry. The photogrammetric record, 33(164), 427-447.
  • Boon, M. A., Greenfield, R., & Tesfamichael, S. (2016). Unmanned aerial vehicle (UAV) photogrammetry produces accurate high-resolution orthophotos, point clouds and surface models for mapping wetlands. South African Journal of Geomatics, 5(2), 186-200.
  • Carbonneau, P. E., & Dietrich, J. T. (2017). Cost‐effective non‐metric photogrammetry from consumer‐grade sUAS: implications for direct georeferencing of structure from motion photogrammetry. Earth surface processes and landforms, 42(3), 473-486.
  • Carrivick, J. L., Smith, M. W., & Quincey, D. J. (2016). Structure from Motion in the Geosciences. John Wiley & Sons.
  • Chen, Y., Chen, Y., & Wang, G. (2019). Bundle adjustment revisited. arXiv preprint arXiv:1912.03858.
  • Clapuyt, F., Vanacker, V., & Van Oost, K. (2016). Reproducibility of UAV-based earth topography reconstructions based on Structure-from-Motion algorithms. Geomorphology, 260, 4-15.
  • Dandois, J. P., Olano, M., & Ellis, E. C. (2015). Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote sensing, 7(10), 13895-13920.
  • de Haas, T., Ventra, D., Carbonneau, P. E., & Kleinhans, M. G. (2014). Debris-flow dominance of alluvial fans masked by runoff reworking and weathering. Geomorphology, 217, 165-181.
  • Dornaika, F., Moujahid, A., El Merabet, Y., & Ruichek, Y. (2016). Building detection from orthophotos using a machine learning approach: An empirical study on image segmentation and descriptors. Expert Systems with Applications, 58, 130-142.
  • Eisenbeiss, H., & Sauerbier, M. (2011). Investigation of UAV systems and flight modes for photogrammetric applications. The Photogrammetric Record, 26(136), 400-421.
  • Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L., & Carbonneau, P. E. (2013). Topographic structure from motion: a new development in photogrammetric measurement. Earth surface processes and Landforms, 38(4), 421-430.
  • Förstner, W., & Wrobel, B. P. (2016). Photogrammetric computer vision. Springer International Publishing Switzerland.
  • Gerke, M., & Przybilla, H. J. (2016). Accuracy analysis of photogrammetric UAV image blocks: Influence of onboard RTK-GNSS and cross flight patterns. Photogrammetrie, Fernerkundung, Geoinformation, (1), 17-30.
  • Granshaw, S. I., & Fraser, C. S. (2015). Computer vision and photogrammetry: Interaction or introspection?. The Photogrammetric Record, 30(149), 3-7.
  • Iglhaut, J., Cabo, C., Puliti, S., Piermattei, L., O’Connor, J., & Rosette, J. (2019). Structure from motion photogrammetry in forestry: A review. Current Forestry Reports, 5, 155-168.
  • Islam, K., Jashimuddin, M., Nath, B., & Nath, T. K. (2018). Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 37-47.
  • Jaud, M., Passot, S., Le Bivic, R., Delacourt, C., Grandjean, P., & Le Dantec, N. (2016). Assessing the accuracy of high resolution digital surface models computed by PhotoScan® and MicMac® in sub-optimal survey conditions. Remote Sensing, 8(6), 465.
  • Javernick, L., Brasington, J., & Caruso, B. (2014). Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry. Geomorphology, 213, 166-182.
  • Juan, L., & Gwun, O. (2009). A comparison of sift, pca-sift and surf. International Journal of Image Processing (IJIP), 3(4), 143-152.
  • Kent, R., Lindsell, J. A., Vaglio Laurin, G., Valentini, R., & Coomes, D. A. (2015). Airborne LiDAR detects selectively logged tropical forest even in an advanced stage of recovery. Remote Sensing, 7(7), 8348-8367.
  • Kersten, T., & Lindstaedt, M. (2022). UAV-basierte Bildflüge mit RTK-GNSS–brauchen wir da noch Passpunkte. DVW-Schriftenreihe Band 100/2022, 39-58.
  • Kersten, T., Wolf, J., & Lindstaedt, M. (2022, May). Investigations into the accuracy of the UAV system Dji Matrice 300 Rtk with the sensors Zenmuse P1 and L1 in the Hamburg test field. In XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, 6–11 June 2022, Nice, France (pp. 339-346). Copernicus.
  • Kršák, B., Blišťan, P., Pauliková, A., Puškárová, P., Kovanič, Ľ. M., Palková, J., & Zelizňaková, V. (2016). Use of low-cost UAV photogrammetry to analyze the accuracy of a digital elevation model in a case study. Measurement, 91, 276-287.
  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60, 91-110.
  • Lucieer, A., Jong, S. M. D., & Turner, D. (2014). Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Progress in physical geography, 38(1), 97-116.
  • Martínez-Carricondo, P., Agüera-Vega, F., Carvajal-Ramírez, F., Mesas-Carrascosa, F. J., García-Ferrer, A., & Pérez-Porras, F. J. (2018). Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points. International journal of applied earth observation and geoinformation, 72, 1-10.
  • Mesas-Carrascosa, F. J., Notario García, M. D., Meroño de Larriva, J. E., & García-Ferrer, A. (2016). An analysis of the influence of flight parameters in the generation of unmanned aerial vehicle (UAV) orthomosaicks to survey archaeological areas. Sensors, 16(11), 1838.
  • Micheletti, N., Chandler, J. H., & Lane, S. N. (2015). Structure from motion (SfM) photogrammetry. Br Soc Geomorphol.
  • Murtiyoso, A., & Grussenmeyer, P. (2017). Documentation of heritage buildings using close‐range UAV images: dense matching issues, comparison and case studies. The Photogrammetric Record, 32(159), 206-229.
  • Naimaee, R., Saadatseresht, M., & Omidalizarandi, M. (2023). Automatic Extraction of Control Points from 3d LIDAR Mobile Mapping and Uav Imagery for Aerial Triangulation. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10, 581-588.
  • Peppa, M. V., Mills, J. P., Moore, P., Miller, P. E., & Chambers, J. E. (2016). Accuracy assessment of a UAV-based landslide monitoring system. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 895-902.
  • Prieto, S. A., Adán, A., & Quintana, B. (2020). Preparation and enhancement of 3D laser scanner data for realistic coloured BIM models. The Visual Computer, 36(1), 113-126.
  • Przybilla, H. J., Bäumker, M., Luhmann, T., Hastedt, H., & Eilers, M. (2020). Interaction between direct georeferencing, control point configuration and camera self-calibration for RTK-based UAV photogrammetry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 485-492.
  • Rangel, J. M. G., Gonçalves, G. R., & Pérez, J. A. (2018). The impact of number and spatial distribution of GCPs on the positional accuracy of geospatial products derived from low-cost UASs. International Journal of Remote Sensing, 39(21), 7154-7171.
  • Rehak, M., & Skaloud, J. (2015). Fixed-wing micro aerial vehicle for accurate corridor mapping. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 23-31.
  • Remondino, F., & El‐Hakim, S. (2006). Image‐based 3D modelling: a review. The photogrammetric record, 21(115), 269-291.
  • Remondino, F., & El‐Hakim, S. (2006). Image‐based 3D modelling: a review. The photogrammetric record, 21(115), 269-291.
  • Reshetyuk, Y., & Mårtensson, S. G. (2016). Generation of highly accurate digital elevation models with unmanned aerial vehicles. The Photogrammetric Record, 31(154), 143-165.
  • Sanz-Ablanedo, E., Chandler, J. H., Rodríguez-Pérez, J. R., & Ordóñez, C. (2018). Accuracy of unmanned aerial vehicle (UAV) and SfM photogrammetry survey as a function of the number and location of ground control points used. Remote Sensing, 10(10), 1606.
  • Smith, M. W., Carrivick, J. L., & Quincey, D. J. (2016). Structure from motion photogrammetry in physical geography. Progress in physical geography, 40(2), 247-275.
  • Snavely, N., Seitz, S. M., & Szeliski, R. (2008). Modeling the world from internet photo collections. International journal of computer vision, 80, 189-210.
  • Stöcker, C., Nex, F., Koeva, M., & Gerke, M. (2017). Quality assessment of combined IMU/GNSS data for direct georeferencing in the context of UAV-based mapping. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 355-361.
  • Turner, D., Lucieer, A., & Wallace, L. (2013). Direct georeferencing of ultrahigh-resolution UAV imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(5), 2738-2745.
  • Ullman, S. (1979). The interpretation of structure from motion. Proceedings of the Royal Society of London. Series B. Biological Sciences, 203(1153), 405-426.
  • Vasuki, Y., Holden, E. J., Kovesi, P., & Micklethwaite, S. (2014). Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: An image analysis approach. Computers & Geosciences, 69, 22-32.
  • Vautherin, J., Rutishauser, S., Schneider-Zapp, K., Choi, H. F., Chovancova, V., Glass, A., & Strecha, C. (2016). Photogrammetric accuracy and modeling of rolling shutter cameras. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 139-146.
  • Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). ‘Structure-from-Motion’photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300-314.
  • Wierzbicki, D., Kedzierski, M., & Fryskowska, A. (2015). Assesment of the influence of UAV image quality on the orthophoto production. The international archives of the photogrammetry, remote sensing and spatial information sciences, 40, 1-8.
  • Zhao, B., Li, J., Wang, L., & Shi, Y. (2020, April). Positioning accuracy assessment of a commercial RTK UAS. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V, 11414, 47-53.
  • Zhao, S. (2021). A Commercial PPK Solution for Phantom 4 RTK. GIM International, Business Guide, (1), 30-32.

UAV Photogrammetry as a Photogrammetric Computer Vision Technique

Year 2023, Volume: 5 Issue: 2, 59 - 71, 31.12.2023

Ozan Arslan

https://doi.org/10.51534/tiha.1392600

Abstract

Unlike conventional photogrammetry, UAV-photogrammetry, which allows the collection of image data taken obliquely from different heights with low-cost non-metric digital cameras, has methods and workflow processes that include the combination of computer vision and photogrammetry. In this context, the Structure from Motion (SFM) technique has become the standard technique for UAV-based orthophoto and 3D terrain model production. For this reason, SFM photogrammetry terminology has also started to be used in the literature. In this study, the distinctions and similarities between these techniques (photogrammetry, UAV-photogrammetry, SfM photogrammetry), which have close terminology and intertwined methods and algorithms, will be analysed conceptually and in terms of application practice.

Keywords

UAV-photogrammetry SfM Multi-view stereo SIFT Bundle adjustment

References

  • Agüera-Vega, F., Carvajal-Ramírez, F., & Martínez-Carricondo, P. (2017). Accuracy of digital surface models and orthophotos derived from unmanned aerial vehicle photogrammetry. Journal of Surveying Engineering, 143(2), 04016025.
  • Amrullah, C., Suwardhi, D., & Meilano, I. (2016). Product accuracy effect of oblique and vertical non-metric digital camera utilization in UAV-photogrammetry to determine fault plane. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 41-48.
  • Balázsik, V., Tóth, Z., & Abdurahmanov, I. (2021). Analysis of Data Acquisition Accuracy with UAV. Int. J. Geoinf, 17, 1-10.
  • Grayson, B., Penna, N. T., Mills, J. P., & Grant, D. S. (2018). GPS precise point positioning for UAV photogrammetry. The photogrammetric record, 33(164), 427-447.
  • Boon, M. A., Greenfield, R., & Tesfamichael, S. (2016). Unmanned aerial vehicle (UAV) photogrammetry produces accurate high-resolution orthophotos, point clouds and surface models for mapping wetlands. South African Journal of Geomatics, 5(2), 186-200.
  • Carbonneau, P. E., & Dietrich, J. T. (2017). Cost‐effective non‐metric photogrammetry from consumer‐grade sUAS: implications for direct georeferencing of structure from motion photogrammetry. Earth surface processes and landforms, 42(3), 473-486.
  • Carrivick, J. L., Smith, M. W., & Quincey, D. J. (2016). Structure from Motion in the Geosciences. John Wiley & Sons.
  • Chen, Y., Chen, Y., & Wang, G. (2019). Bundle adjustment revisited. arXiv preprint arXiv:1912.03858.
  • Clapuyt, F., Vanacker, V., & Van Oost, K. (2016). Reproducibility of UAV-based earth topography reconstructions based on Structure-from-Motion algorithms. Geomorphology, 260, 4-15.
  • Dandois, J. P., Olano, M., & Ellis, E. C. (2015). Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote sensing, 7(10), 13895-13920.
  • de Haas, T., Ventra, D., Carbonneau, P. E., & Kleinhans, M. G. (2014). Debris-flow dominance of alluvial fans masked by runoff reworking and weathering. Geomorphology, 217, 165-181.
  • Dornaika, F., Moujahid, A., El Merabet, Y., & Ruichek, Y. (2016). Building detection from orthophotos using a machine learning approach: An empirical study on image segmentation and descriptors. Expert Systems with Applications, 58, 130-142.
  • Eisenbeiss, H., & Sauerbier, M. (2011). Investigation of UAV systems and flight modes for photogrammetric applications. The Photogrammetric Record, 26(136), 400-421.
  • Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L., & Carbonneau, P. E. (2013). Topographic structure from motion: a new development in photogrammetric measurement. Earth surface processes and Landforms, 38(4), 421-430.
  • Förstner, W., & Wrobel, B. P. (2016). Photogrammetric computer vision. Springer International Publishing Switzerland.
  • Gerke, M., & Przybilla, H. J. (2016). Accuracy analysis of photogrammetric UAV image blocks: Influence of onboard RTK-GNSS and cross flight patterns. Photogrammetrie, Fernerkundung, Geoinformation, (1), 17-30.
  • Granshaw, S. I., & Fraser, C. S. (2015). Computer vision and photogrammetry: Interaction or introspection?. The Photogrammetric Record, 30(149), 3-7.
  • Iglhaut, J., Cabo, C., Puliti, S., Piermattei, L., O’Connor, J., & Rosette, J. (2019). Structure from motion photogrammetry in forestry: A review. Current Forestry Reports, 5, 155-168.
  • Islam, K., Jashimuddin, M., Nath, B., & Nath, T. K. (2018). Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 37-47.
  • Jaud, M., Passot, S., Le Bivic, R., Delacourt, C., Grandjean, P., & Le Dantec, N. (2016). Assessing the accuracy of high resolution digital surface models computed by PhotoScan® and MicMac® in sub-optimal survey conditions. Remote Sensing, 8(6), 465.
  • Javernick, L., Brasington, J., & Caruso, B. (2014). Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry. Geomorphology, 213, 166-182.
  • Juan, L., & Gwun, O. (2009). A comparison of sift, pca-sift and surf. International Journal of Image Processing (IJIP), 3(4), 143-152.
  • Kent, R., Lindsell, J. A., Vaglio Laurin, G., Valentini, R., & Coomes, D. A. (2015). Airborne LiDAR detects selectively logged tropical forest even in an advanced stage of recovery. Remote Sensing, 7(7), 8348-8367.
  • Kersten, T., & Lindstaedt, M. (2022). UAV-basierte Bildflüge mit RTK-GNSS–brauchen wir da noch Passpunkte. DVW-Schriftenreihe Band 100/2022, 39-58.
  • Kersten, T., Wolf, J., & Lindstaedt, M. (2022, May). Investigations into the accuracy of the UAV system Dji Matrice 300 Rtk with the sensors Zenmuse P1 and L1 in the Hamburg test field. In XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, 6–11 June 2022, Nice, France (pp. 339-346). Copernicus.
  • Kršák, B., Blišťan, P., Pauliková, A., Puškárová, P., Kovanič, Ľ. M., Palková, J., & Zelizňaková, V. (2016). Use of low-cost UAV photogrammetry to analyze the accuracy of a digital elevation model in a case study. Measurement, 91, 276-287.
  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60, 91-110.
  • Lucieer, A., Jong, S. M. D., & Turner, D. (2014). Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Progress in physical geography, 38(1), 97-116.
  • Martínez-Carricondo, P., Agüera-Vega, F., Carvajal-Ramírez, F., Mesas-Carrascosa, F. J., García-Ferrer, A., & Pérez-Porras, F. J. (2018). Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points. International journal of applied earth observation and geoinformation, 72, 1-10.
  • Mesas-Carrascosa, F. J., Notario García, M. D., Meroño de Larriva, J. E., & García-Ferrer, A. (2016). An analysis of the influence of flight parameters in the generation of unmanned aerial vehicle (UAV) orthomosaicks to survey archaeological areas. Sensors, 16(11), 1838.
  • Micheletti, N., Chandler, J. H., & Lane, S. N. (2015). Structure from motion (SfM) photogrammetry. Br Soc Geomorphol.
  • Murtiyoso, A., & Grussenmeyer, P. (2017). Documentation of heritage buildings using close‐range UAV images: dense matching issues, comparison and case studies. The Photogrammetric Record, 32(159), 206-229.
  • Naimaee, R., Saadatseresht, M., & Omidalizarandi, M. (2023). Automatic Extraction of Control Points from 3d LIDAR Mobile Mapping and Uav Imagery for Aerial Triangulation. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10, 581-588.
  • Peppa, M. V., Mills, J. P., Moore, P., Miller, P. E., & Chambers, J. E. (2016). Accuracy assessment of a UAV-based landslide monitoring system. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 895-902.
  • Prieto, S. A., Adán, A., & Quintana, B. (2020). Preparation and enhancement of 3D laser scanner data for realistic coloured BIM models. The Visual Computer, 36(1), 113-126.
  • Przybilla, H. J., Bäumker, M., Luhmann, T., Hastedt, H., & Eilers, M. (2020). Interaction between direct georeferencing, control point configuration and camera self-calibration for RTK-based UAV photogrammetry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 485-492.
  • Rangel, J. M. G., Gonçalves, G. R., & Pérez, J. A. (2018). The impact of number and spatial distribution of GCPs on the positional accuracy of geospatial products derived from low-cost UASs. International Journal of Remote Sensing, 39(21), 7154-7171.
  • Rehak, M., & Skaloud, J. (2015). Fixed-wing micro aerial vehicle for accurate corridor mapping. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 23-31.
  • Remondino, F., & El‐Hakim, S. (2006). Image‐based 3D modelling: a review. The photogrammetric record, 21(115), 269-291.
  • Remondino, F., & El‐Hakim, S. (2006). Image‐based 3D modelling: a review. The photogrammetric record, 21(115), 269-291.
  • Reshetyuk, Y., & Mårtensson, S. G. (2016). Generation of highly accurate digital elevation models with unmanned aerial vehicles. The Photogrammetric Record, 31(154), 143-165.
  • Sanz-Ablanedo, E., Chandler, J. H., Rodríguez-Pérez, J. R., & Ordóñez, C. (2018). Accuracy of unmanned aerial vehicle (UAV) and SfM photogrammetry survey as a function of the number and location of ground control points used. Remote Sensing, 10(10), 1606.
  • Smith, M. W., Carrivick, J. L., & Quincey, D. J. (2016). Structure from motion photogrammetry in physical geography. Progress in physical geography, 40(2), 247-275.
  • Snavely, N., Seitz, S. M., & Szeliski, R. (2008). Modeling the world from internet photo collections. International journal of computer vision, 80, 189-210.
  • Stöcker, C., Nex, F., Koeva, M., & Gerke, M. (2017). Quality assessment of combined IMU/GNSS data for direct georeferencing in the context of UAV-based mapping. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 355-361.
  • Turner, D., Lucieer, A., & Wallace, L. (2013). Direct georeferencing of ultrahigh-resolution UAV imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(5), 2738-2745.
  • Ullman, S. (1979). The interpretation of structure from motion. Proceedings of the Royal Society of London. Series B. Biological Sciences, 203(1153), 405-426.
  • Vasuki, Y., Holden, E. J., Kovesi, P., & Micklethwaite, S. (2014). Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: An image analysis approach. Computers & Geosciences, 69, 22-32.
  • Vautherin, J., Rutishauser, S., Schneider-Zapp, K., Choi, H. F., Chovancova, V., Glass, A., & Strecha, C. (2016). Photogrammetric accuracy and modeling of rolling shutter cameras. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 139-146.
  • Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). ‘Structure-from-Motion’photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300-314.
  • Wierzbicki, D., Kedzierski, M., & Fryskowska, A. (2015). Assesment of the influence of UAV image quality on the orthophoto production. The international archives of the photogrammetry, remote sensing and spatial information sciences, 40, 1-8.
  • Zhao, B., Li, J., Wang, L., & Shi, Y. (2020, April). Positioning accuracy assessment of a commercial RTK UAS. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V, 11414, 47-53.
  • Zhao, S. (2021). A Commercial PPK Solution for Phantom 4 RTK. GIM International, Business Guide, (1), 30-32.
There are 53 citations in total.

Details

Primary Language Turkish
Subjects Photogrametry
Journal Section Research Articles
Authors

Ozan Arslan 0000-0003-1441-2965

Early Pub Date December 22, 2023
Publication Date December 31, 2023
Submission Date November 18, 2023
Acceptance Date December 19, 2023
Published in Issue Year 2023 Volume: 5 Issue: 2

Cite

APA Arslan, O. (2023). Bir Fotogrametrik Bilgisayarlı Görü Tekniği olarak İHA Fotogrametrisi. Türkiye İnsansız Hava Araçları Dergisi, 5(2), 59-71. https://doi.org/10.51534/tiha.1392600
AMA Arslan O. Bir Fotogrametrik Bilgisayarlı Görü Tekniği olarak İHA Fotogrametrisi. tiha. December 2023;5(2):59-71. doi:10.51534/tiha.1392600
Chicago Arslan, Ozan. “Bir Fotogrametrik Bilgisayarlı Görü Tekniği Olarak İHA Fotogrametrisi”. Türkiye İnsansız Hava Araçları Dergisi 5, no. 2 (December 2023): 59-71. https://doi.org/10.51534/tiha.1392600.
EndNote Arslan O (December 1, 2023) Bir Fotogrametrik Bilgisayarlı Görü Tekniği olarak İHA Fotogrametrisi. Türkiye İnsansız Hava Araçları Dergisi 5 2 59–71.
IEEE O. Arslan, “Bir Fotogrametrik Bilgisayarlı Görü Tekniği olarak İHA Fotogrametrisi”, tiha, vol. 5, no. 2, pp. 59–71, 2023, doi: 10.51534/tiha.1392600.
ISNAD Arslan, Ozan. “Bir Fotogrametrik Bilgisayarlı Görü Tekniği Olarak İHA Fotogrametrisi”. Türkiye İnsansız Hava Araçları Dergisi 5/2 (December 2023), 59-71. https://doi.org/10.51534/tiha.1392600.
JAMA Arslan O. Bir Fotogrametrik Bilgisayarlı Görü Tekniği olarak İHA Fotogrametrisi. tiha. 2023;5:59–71.
MLA Arslan, Ozan. “Bir Fotogrametrik Bilgisayarlı Görü Tekniği Olarak İHA Fotogrametrisi”. Türkiye İnsansız Hava Araçları Dergisi, vol. 5, no. 2, 2023, pp. 59-71, doi:10.51534/tiha.1392600.
Vancouver Arslan O. Bir Fotogrametrik Bilgisayarlı Görü Tekniği olarak İHA Fotogrametrisi. tiha. 2023;5(2):59-71.
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