Araştırma Makalesi
Vatandaş Bilimi Tabanlı Vektör Haritaların Kullanılması ile İnsansız Hava Aracından Elde Edilen Görüntülerin Yöneltilmesi için Otomatik Yer Kontrol Noktası Üretimi
Öz
Son yıllarda insansız hava araçları teknolojisi hızlı bir gelişim göstermiştir. Bu yeni teknoloji ile hızlı, etkin görüntü alımı yapılabilmekte, ayrıca görüntü verileri işlenerek hassas harita, ortogörüntü ve sayısal arazi modeli oluşturulabilmektedir. IHA’lardan elde edilen görüntülerin bu amaçlarla kullanımı için fotogrametride mutlak yöneltmenin yapılabilmesi için, eğer kamera çekim konuları bilinmiyorsa alım yapılan alanda koordinatı bilinen noktalara yani diğer bir deyişle yer kontrol noktalarına ihtiyaç bulunmaktadır. Bu noktalar geleneksel olarak arazi çalışmaları (total station, GNSS sistemleri vb.) ile elde edilmektedir, zaman alıcıdır. Bunun yanında, mevcut doğruluk derecesi bilinen mevcut harita, uydu görüntüsü gibi mekânsal veriler de yer kontrol noktası sağlayacak potansiyele sahiptir. Bu çalışmada mevcut vektör haritaların kullanımıyla, istenen doğruluk derecesinin kullanılan dış verinin kalitesine bağlı olarak, yer kontrol noktalarının kullanımını ortadan kaldırmak amaçlanmıştır. Bu amaçla güncellenen en bilinen vatandaş bilimi uygulamalarından ‘OpenStreet Map’ vektör haritalar (OSM) ve IHA’lardan elde edilen görüntülerle otomatik eşleştirilmesi ile yer kontrol noktaları üretilmiş, ortogörüntü ve sayısal yükseklik modeli oluşturulmuştur. Çalışmanın sonuçlarının kalitesi, mevcut detaylı oluşturulmuş bir vektör haritadan elle elde edilen yer kontrol noktalarının, üretilen noktalarla karşılaştırılması ve üretilen sayısal yükseklik modellerinin üç boyutlu karşılaştırılması ile analiz edilmiştir.
Anahtar Kelimeler
İnsansız hava aracı Veri eşleştirme Vatandaş bilimi Uzaktan algılama
Kaynakça
- 1. Kounadi, O., 2009. Assessing the Quality of Open Street Map Data. Department of Civil, Environmental and Geomatic Engineering, University College of London, Yüksek Lisans Tezi, 81, Londra.
- 2. Zulfiqar, N., 2008. A Study of the Quality of openstreetmap.org Map-a Comparison of OSM Data and Ordnance Survey Data, Yüksek Lisans Tezi, 54, Londra.
- 3. Fan, H., Zipf, A., Fu, Q., Neis, P., 2014. Quality Assessment for Building Footprints Data on Openstreetmap, International Journal of Geographical Information Science, 28(4), 700-719.
- 4. http://www.marketsandmarkets.com, 2013. Alıntı Tarihi 06.11.2017.
- 5. Cho, G., Hildebrand, A., Claussen, J., Cosyn, P., Morris, S., 2013. Pilotless Aerial Vehicle Systems: Size, Scale and Functions, Coordinates, 9(1), 8-16.
- 6. Mayr, W., 2013. Unmanned Aerial Systems-for the Rest of Us. 54th Photogrammetric Week, Institut für Photogrammetrie, 9-13 Eylül 2013, 151-163, Stuttgart.
- 7. Petrie, G., 2013. Commercial Operation of Lightweight UAVs for Aerial Imaging and Mapping, GEOInformatics 16, 28-39.
- 8. Colomina, I., Molina, P., 2014. Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review, ISPRS Journal of Photogrammetry and Remote Sensing, 92(1), 79-97.
- 9. Przybilla, H., Wester-Ebbinghaus, W., 1979. Bildflug mit ferngelenktem Kleinflugzeug Bildmessung und Luftbildwessen, 47, 137-142.
- 10. Wester-Ebbinghaus, W., 1980. Aerial Photography by Radio Controlled Model Helicopter, Photogrammetric Record, 10, 85-92.
- 11. Dowman, I., 2012. More than Information from Imagery, Geospatial World, 2, 34-44.
- 12. Cramer, M., 2013. The UAV LGL BW Projecta NMCA Case Study, 54th Photogrammetric Week, Institut für Photogrammetrie, Universität Stuttgart, 9-13 Eylül 2013, Stuttgart, 165-179.
- 13. Mayr, W., 2013. Unmanned Aerial Systems-for the Rest of Us. 54th Photogrammetric Week, Institut für Photogrammetrie, Universität Stuttgart, 9-13 Eylül 2013, 151-163, Stuttgart.
- 14. Rehak, M., Mabillard, R., Skaloud, J., 2013. A Micro-UAV with the Capability of Direct Georeferencing, International Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41(W2), 317-323.
- 15. Mostafa, M.M.R., Hutton, J., Lithopoulos, E., 2001. Airborne Direct Georeferencing of Frame Imagery: An Error Budget. The 3rd International Symposium on Mobile Mapping Technology, 3-5 Ocak, Kahire.
- 16. Skaloud, J., 1999. Problems in DirectGeoreferencing by INS/DGPS in the Airborne Environment. ISPRS Workshop Barcelona, 25-26 Kasım, Barselona.
- 17. Grejner-Brzezinska, D.A., 1999. Direct Exterior Orientation of Airborne Imagery with GPS/INS System: Performance Analysis, Navigation, 46(4), 261-270.
- 18. Remondino, F., Barazzetti, L., Sarazzi, D., 2009. Uav Photogrammetry for Mapping and 3D Modeling-Current Status and Future Perspectives, International Conference on Unmanned Aerial Vehicle in Geomatics, 14–16 Eylül, 25-31, Zurih.
- 19. Laliberte, A., Rango, A., 2008. A Procedure for Orthorectification of Sub-decimeter Resolution Imagery Obtained with an Unmanned Aerial Vehicle (UAV) ASPRS 2008 Annual Conference, 28 Nisan-2 Mayıs, Portland.
- 20. Tanathong, S., Lee, I., 2014. Using GPS/INS Data to Enhance Image Matching for Real-time Aerial Triangulation, Computers & Geosciences, 72(1), 244-254.
- 21. Li, C., Zhang, G., Lei, T., Gong, A., 2011. Quick Image-processing Method of UAV Without Control Points Data in Earthquake Disaster Area, Transactions of Nonferrous Metals Society of China, 21(3), 523-528.
- 22. Xiang, H., Tian, L., 2011. Method for Automatic Georeferencing Aerial Remote Sensing (RS) Images from an Unmanned Aerial Vehicle (UAV) Platform, Biosystems Engineering, 108(2), 104-113.
- 23. Liew, L.H., Lee, B.Y., Wang, Y.C., Cheah, W.S., 2012. Simulation Study on Distribution of Control Points for Aerial Images Rectification, Lecture Notes in Electrical Engineering, 203(1), 169-177.
- 24. Pateraki, M., 2005. Adaptive Multi-Image Matching for DSM Generation from Airborne Linear Array 341 CCD Data. Eth Zurich Institute of Geodesy and Photogrammetry Doktora Tezi, No. 86, Institute of Geodesy and Photogrammetry, ETH Zurich, 342, Zürih.
- 25. Grompone von Gioi, R., Jakubowicz, J., Morel, JM., Randall, G., 2012. LSD: a Line Segment Detector, Image Processing On Line, 2, 35-55.
- 26. Ball, G.H., Hall, D.J., 1965. ISODATA, A Novel Method of Data Analysis and Pattern Classification, Teknik Rapor, Stanford Research Institute, 79.
Automated Generation of Ground Control Points for Oriention of UAV Images by Matching with Citizen Science-Based Vector Maps
Öz
In recent years, unmanned aerial vehicles technology has developed rapidly. With this new technology, fast and effective image acquisition can be performed, and accurate map, orthogonal image and digital terrain model can be created by processing image data.In order to use the images obtained from the UAVs for these purposes, it is necessary to perform the interior and externaior orientation of the acquired images and to establish the ground control points for this purpose. These points are obtained by land surveys, and it is time consuming and expensive. On the other hand, the existing geodata have high potantial to produce the ground control points with their accuracy. With this work, it is aimed to eliminate the use of ground control points with the use of existing geospatial data, a citizen science application existing openstreetmap (OSM) vector maps are used. The quality of the results of the study was evaluated by comparing the ground control points measured on the existing accurate map which created with land surveying. The comparison of the 3D profiles and surface models is also performed.
Anahtar Kelimeler
Unmanned air vehicle Data matching Citizen-science Remote sensing
Kaynakça
- 1. Kounadi, O., 2009. Assessing the Quality of Open Street Map Data. Department of Civil, Environmental and Geomatic Engineering, University College of London, Yüksek Lisans Tezi, 81, Londra.
- 2. Zulfiqar, N., 2008. A Study of the Quality of openstreetmap.org Map-a Comparison of OSM Data and Ordnance Survey Data, Yüksek Lisans Tezi, 54, Londra.
- 3. Fan, H., Zipf, A., Fu, Q., Neis, P., 2014. Quality Assessment for Building Footprints Data on Openstreetmap, International Journal of Geographical Information Science, 28(4), 700-719.
- 4. http://www.marketsandmarkets.com, 2013. Alıntı Tarihi 06.11.2017.
- 5. Cho, G., Hildebrand, A., Claussen, J., Cosyn, P., Morris, S., 2013. Pilotless Aerial Vehicle Systems: Size, Scale and Functions, Coordinates, 9(1), 8-16.
- 6. Mayr, W., 2013. Unmanned Aerial Systems-for the Rest of Us. 54th Photogrammetric Week, Institut für Photogrammetrie, 9-13 Eylül 2013, 151-163, Stuttgart.
- 7. Petrie, G., 2013. Commercial Operation of Lightweight UAVs for Aerial Imaging and Mapping, GEOInformatics 16, 28-39.
- 8. Colomina, I., Molina, P., 2014. Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review, ISPRS Journal of Photogrammetry and Remote Sensing, 92(1), 79-97.
- 9. Przybilla, H., Wester-Ebbinghaus, W., 1979. Bildflug mit ferngelenktem Kleinflugzeug Bildmessung und Luftbildwessen, 47, 137-142.
- 10. Wester-Ebbinghaus, W., 1980. Aerial Photography by Radio Controlled Model Helicopter, Photogrammetric Record, 10, 85-92.
- 11. Dowman, I., 2012. More than Information from Imagery, Geospatial World, 2, 34-44.
- 12. Cramer, M., 2013. The UAV LGL BW Projecta NMCA Case Study, 54th Photogrammetric Week, Institut für Photogrammetrie, Universität Stuttgart, 9-13 Eylül 2013, Stuttgart, 165-179.
- 13. Mayr, W., 2013. Unmanned Aerial Systems-for the Rest of Us. 54th Photogrammetric Week, Institut für Photogrammetrie, Universität Stuttgart, 9-13 Eylül 2013, 151-163, Stuttgart.
- 14. Rehak, M., Mabillard, R., Skaloud, J., 2013. A Micro-UAV with the Capability of Direct Georeferencing, International Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41(W2), 317-323.
- 15. Mostafa, M.M.R., Hutton, J., Lithopoulos, E., 2001. Airborne Direct Georeferencing of Frame Imagery: An Error Budget. The 3rd International Symposium on Mobile Mapping Technology, 3-5 Ocak, Kahire.
- 16. Skaloud, J., 1999. Problems in DirectGeoreferencing by INS/DGPS in the Airborne Environment. ISPRS Workshop Barcelona, 25-26 Kasım, Barselona.
- 17. Grejner-Brzezinska, D.A., 1999. Direct Exterior Orientation of Airborne Imagery with GPS/INS System: Performance Analysis, Navigation, 46(4), 261-270.
- 18. Remondino, F., Barazzetti, L., Sarazzi, D., 2009. Uav Photogrammetry for Mapping and 3D Modeling-Current Status and Future Perspectives, International Conference on Unmanned Aerial Vehicle in Geomatics, 14–16 Eylül, 25-31, Zurih.
- 19. Laliberte, A., Rango, A., 2008. A Procedure for Orthorectification of Sub-decimeter Resolution Imagery Obtained with an Unmanned Aerial Vehicle (UAV) ASPRS 2008 Annual Conference, 28 Nisan-2 Mayıs, Portland.
- 20. Tanathong, S., Lee, I., 2014. Using GPS/INS Data to Enhance Image Matching for Real-time Aerial Triangulation, Computers & Geosciences, 72(1), 244-254.
- 21. Li, C., Zhang, G., Lei, T., Gong, A., 2011. Quick Image-processing Method of UAV Without Control Points Data in Earthquake Disaster Area, Transactions of Nonferrous Metals Society of China, 21(3), 523-528.
- 22. Xiang, H., Tian, L., 2011. Method for Automatic Georeferencing Aerial Remote Sensing (RS) Images from an Unmanned Aerial Vehicle (UAV) Platform, Biosystems Engineering, 108(2), 104-113.
- 23. Liew, L.H., Lee, B.Y., Wang, Y.C., Cheah, W.S., 2012. Simulation Study on Distribution of Control Points for Aerial Images Rectification, Lecture Notes in Electrical Engineering, 203(1), 169-177.
- 24. Pateraki, M., 2005. Adaptive Multi-Image Matching for DSM Generation from Airborne Linear Array 341 CCD Data. Eth Zurich Institute of Geodesy and Photogrammetry Doktora Tezi, No. 86, Institute of Geodesy and Photogrammetry, ETH Zurich, 342, Zürih.
- 25. Grompone von Gioi, R., Jakubowicz, J., Morel, JM., Randall, G., 2012. LSD: a Line Segment Detector, Image Processing On Line, 2, 35-55.
- 26. Ball, G.H., Hall, D.J., 1965. ISODATA, A Novel Method of Data Analysis and Pattern Classification, Teknik Rapor, Stanford Research Institute, 79.
Toplam 26 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Aralık 2018 |
| Yayımlandığı Sayı | Yıl 2018 Cilt: 33 Sayı: 4 |