GeoEtrim 2020 sürümü: Konumsal görüntü değerlendirme için akademik bir yazılım

Abstract

GeoEtrim (Geospatial Evaluation and Training of Images), optik görüntüleri konumsal olarak değerlendirmek için geliştirilen akademik yazılım paketler bütünüdür. GeoEtrim geçmişte MATLAB ortamında geliştirilmiş olsa da, çok büyük boyutlu görüntü dosyalarının içe aktarılması/okunması, yer kontrol noktalarının ve bağımsız denetim noktalarının kolay bir şekilde toplanması ve çeşitli hesaplamaların yapılmasındaki zorlukların üstesinden gelinmesi için başka bir programlama dilinde (C #) derlenmesine karar verilmiştir. Proje oluşturmak, görüntüleri içe aktarmak, görüntünün zıtlığını geliştirmek, yer kontrol noktaları ve bağımsız denetim noktalarını toplamak, GeoTransform alt paketi aracılığıyla algılayıcıdan bağımsız yöneltme yöntemlerini çalıştırmak mevcut sürümün sunduğu başlıca özelliklerdir. Ek olarak, uyuşumsuz ölçü testi ve parametre anlamlılık testleri de özellik olarak eklenmiştir. GeoEtrim akademik çalışmalar için ücretsiz olarak kullanılabilir ve böylelikle kullanıcı topluluğu her geçen gün artabilir. Bu makalede GeoEtrim 2020'nin hem kuramsal yapısı hem de grafik kullanıcı arayüzü tanıtılmaktadır.

Keywords

• GeoEtrim
• Koordinat dönüşümü
• Algılayıcı yöneltmesi
• Grafik kullanıcı arayüzü
• Konumsal bilgi

GeoEtrim 2020 executed version: an academic software for geospatial image evaluation

Abstract

GeoEtrim (Geospatial Evaluation and Training of Images) is a set of academic software packages to evaluate optical images geospatially. Although GeoEtrim was developed in the MATLAB environment in the past, it was decided to be compiled in another programming language (C#) in order to overcome the challenges like importing/reading very big sized image files, collecting Ground Control Point (GCP) and Independent Check Point (ICP) in an easy way and running the orientation models, etc. Creating projects, importing image(s), enhancing the image contrast, collecting GCP&ICP and running sensor independent orientation models through sub-package GeoTransform are the major opportunities of the current version. In addition, GeoTransform itself enables blunder and parameter validation tests. GeoEtrim can also be used free-of-charge for academic purposes; therefore, its user community increases rapidly. This paper focus on both theoretical background and graphical user interface of GeoEtrim 2020 executed version.

Keywords

• GeoEtrim
• Coordinate transformation
• Sensor orientation
• Graphical user interface
• Geospatial information

References

1. Abdel-Aziz, Y. I., & Karara, M. (1971). Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. American Society of Photogrammetry: Symposium on Close-Range Photogrammetry, 1-18.
2. Aguilera, D. G., & Lahoz, J. G. (2006). sv3DVision: didactical photogrammetric software for single image-based modeling. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(6), 171-179.
3. AlphaBeta (2017). The Economic Impact of Geospatial Services: How Consumers, Businesses and Society Benefit from Location-Based Information. https://storage.googleapis.com/valueoftheweb/pdfs/GeoSpatial%2520FA_Pages-compressed%2520%25282%2529.pdf (Accessed: 12 August 2018)
4. Ayoub, F., Leprince, S., & Keene, L. (2009). User’s guide to COSI-CORR co-registration of optically sensed images and correlation. California Institute of Technology: Pasadena, CA, USA, 38.
5. Aytekin, G., Topan, H., Elkar, Y. E., Kisi, M., & Erisik, O. (2019). 2D Orientation Accuracy of Göktürk-1 Panchromatic Imagery. 2019 9th International Conference on Recent Advances in Space Technologies (RAST), 821-826.
6. Baarda, W. (1968). A Testing Procedure for Use in Geodetic Networks. Publication on Geodesy, 2(5), 97.
7. Buyuksalih, G., Akcin, H., & Jacobsen, K. (2006). Geometry of OrbView-3 Images. ISPRS Workshop on Topographic Mapping From Space (with Special Emphasis on Small Satellites).
8. Cam, A. (2018). Algılayıcıdan Bağımsız Dönüşüm Yöntemleri İle Üretilen Ortogörüntülerin Konum Doğruluğunun Belirlenmesi (Master Thesis). Zonguldak Bulent Ecevit University, Graduate Scholl of Natural and Applied Sciences, Zonguldak, Turkey (in Turkish).

9. Chen, J., Dowman, I., Li, S., Li, Z., Madden, M., Mills, J., Paparoditis, N., Rottensteiner, F., Sester, M., Toth, C., Trinder, J., & Heipke, C. (2016). Information from imagery: ISPRS scientific vision and research agenda. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 3-21.
10. CloudCompare (2020). CloudCompare: 3D point cloud and mesh processing software. https://www.cloudcompare.org/ (Accessed: 27 August 2018).
11. COLMAP (2020). https://demuc.de/colmap/ (Accessed: 27 August 2018).
12. CRCSI (2020). http://www.crcsi.com.au/impact/barista/ (Accessed: 27 August 2018).
13. Dowman, I. (2010). Need to Reach Out to Other Sciences. (Durk Haarsma, Publishing Director, GIM International), https://www.gim-international.com/content/article/need-to-reach-out-to-other-sciences (Accessed: 30 August 2018).
14. Euroconsult (2017). Satellite-based Earth Observation Market: Strong Growth and Fierce Competition. https://www.gim-international.com/content/news/satellite-based-earth-observation-market-strong-growth-and-fierce-competition (Accessed: 30 August 2018).
15. European Space Agency (ESA) (2020). Sentinel Application Platform (SNAP). http://step.esa.int/main/toolboxes/snap/ (Accessed: 30 August 2018). FORSAT (2018). A Satellite Image Processing Platform for High Resolution Forest Assessment. http://forsat.eu (Accessed: 30 December 2018).
16. Fuhrmann, S., Langguth, F., & Goesele, M. (2014). MVE-A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage. Furukawa, Y., & Ponce, J. (2018). Patch-based Multi-view Stereo Software (PMVS). https://www.di.ens.fr/pmvs/ (Accessed: 30 December 2018).
17. Furukawa, Y. (2020). Clustering Views for Multi-view Stereo (CMVS). https://www.di.ens.fr/cmvs (Accessed: 27 August 2020).
18. GeoEtrim (2020). GeoEtrim: Geospatial Evaluation and Training of Images. www.geoetrim.org (Accessed: 27 August 2020).
19. González-Aguilera, D., Guerrero, D., Hernández López, D., Rodriguez-Gonzálvez, P., Pierrot, M., & Fernández-Hernández, J. (2012). PW, Photogrammetry Workbench. CATCON Silver Award, ISPRS WG VI/2. Proceedings of the 22nd ISPRS Congress.
20. Gonzalez‐Aguilera, D., López‐Fernández, L., Rodriguez‐Gonzalvez, P., Hernandez‐Lopez, D., Guerrero, D., Remondino, F., Menna, F., Nocerino, E., Toschi, I., Ballabeni, A., & Gaiani, M. (2018). GRAPHOS–open‐source software for photogrammetric applications. The Photogrammetric Record, 33(161), 11-29.
21. Grizonnet, M., Michel, J., Poughon, V., Inglada, J., Savinaud, M., & Cresson, R. (2017). Orfeo ToolBox: open source processing of remote sensing images. Open Geospatial Data, Software and Standards, 2(1), 1-8.
22. Hejlsberg, A., Wiltamuth, S., & Golde, P. (2003). C# language specification. Addison-Wesley Longman Publishing Co., Inc.
23. Institut Géographique National (IGN), & l'École de la Géomatique (ENSG). (2020). MicMac. https://micmac.ensg.eu/index.php/Accueil (Accessed: 27 August 2020).
24. Jacobsen, K. (2003). Geometric potential of IKONOS-and QuickBird-images. Photogrammetric Weeks ‘03, 101-110.
25. Jacobsen, K. (2008). BLUH bundle block adjustment. Program User Manual, https://www.ipi.uni-hannover.de/fileadmin/institut/pdf/ BLUHinfo.pdf (Accessed: 27 August 2020).
26. Koch, K.-R. (1999). Parameter estimation and hypothesis testing in linear models. Springer Science & Business Media.
27. Leprince, S., Barbot, S., Ayoub, F., & Avouac, J. P. (2007). Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements. IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1529-1558.
28. Luhmann, T. (2016). Learning photogrammetry with interactive software tool PhoX. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 39.
29. MeshLab (2020). MeshLab: the open source system for processing and editing 3D triangular meshes. http://www.meshlab.net/ (Accessed: 27 August 2020).
30. Moghaddam, S. H. A., Mokhtarzade, M., Naeini, A. A., & Amiri-Simkooei, A. (2018). A statistical variable selection solution for RFM ill-posedness and overparameterization problems. IEEE Transactions on Geoscience and Remote sensing, 56(7), 3990-4001.
31. Mota, G. L. A., Ribeiro, J. A., Bernardo Filho, O., Silveira, M. T., de Aguiar, R. A., da Silva Badolato, I., da Costa, S. L., & Reolon, P. F. (2012). The e-foto project and the research to implement a GNU/GPL open source educational digital photogrammetric workstation. Geospatial free and open source software in the 21st century, 89-106. Springer: Berlin, Heidelberg.
32. Moulon, P., & Bezzi, A. (2011). Python photogrammetry toolbox: a free solution for three-dimensional documentation. ArcheoFoss, 1-12.
33. Open Source Geospatial Foundation (OSGeo). (2020). OSGeo Projects. https://www.osgeo.org/projects (Accessed: 30 August 2020).
34. Paganini, M., Petiteville, I., Ward, S., Dyke, G., Steventon, M., Harry, J., & Kerblat, F. (2018). Satellite earth observations in support of the sustainable development goals. The CEOS Earth Observation Handbook.
35. Rothermel, M., & Wenzel, K. (2020). SURE (Photogrammetric Surface Reconstruction from Imagery). http://www.ifp.uni-stuttgart.de /publications/software/sure/index.en.html (Accessed: 30 August 2020).
36. Seedahmed, G., & Schenk, T. (2001). Comparative Study of Two Approaches For Deriving The Camera parameters From Direct Linear Transformation. Annual Conference of ASPRS.
37. Shi, S. (2013). Emgu CV Essentials. Packt Publishing Ltd.
38. Snavely, N. (2020). Bundler: Structure from Motion (SfM) for Unordered Image Collections. http://www.cs.cornell.edu/~snavely/bundler/ (Acessed: 30 August 2020).
39. Stallmann, D. (2020). DGAP (Bundle Adjustment). http://www.ifp.uni-stuttgart.de/publications/software/openbundle/index.en.html (Accessed: 30 August 2020).
40. Terlemezoglu, B., & Topan, H. (2020). Eigenvalue-Based Approaches for Solving an Ill-Posed Problem Arising in Sensor Orientation. IEEE Transactions on Geoscience and Remote Sensing, 58(3), 1920-1930.
41. Theia (2020). Theia Vision Library. http://www.theia-sfm.org/ (Accessed: 30 August 2020).
42. Topan, H. (2004). Yörünge Düzeltmeli IRS-1C/1D Pankromatik Mono Görüntüsünün Geometrik Doğruluk ve Bilgi İçeriği Açısından İncelenmesi (Master Thesis). Zonguldak Karaelmas University, Graduate Scholl of Natural and Applied Sciences, Zonguldak, Turkey (in Turkish).
43. Topan, H., Buyuksalih, G., & Kocak, G. (2005) IRS-1C Düzey 1B Görüntüsünün Geometrik Analizinin Sensör Yöneltme Modelleriyle ve Değişik Referans Verileriyle İrdelenmesi. UCTEA Chamber of Survey and Cadastre Engineers, 10. Turkey Scientific and Techical Conference, Ankara, Turkey.
44. Topan, H. (2009). Geometric Analysis of High Resolution Space Images Using Parametric Approaches Considering Satellite Orbital Parameters (PhD Thesis). Istanbul Technical University, Graduate Scholl of Natural and Applied Sciences, Istanbul, Turkey.
45. Topan, H., & Kutoglu, H. S. (2009). Georeferencing accuracy assessment of high-resolution satellite images using figure condition method. IEEE Transactions on Geoscience and Remote Sensing, 47(4), 1256-1261.
46. Topan, H. (2013). First Experience with Figure Condition Analysis Aided Bias Compensated Rational Function Model for Georeferencing of High Resolution Satellite Images. Journal of the Indian Society of Remote Sensing, 41(4), 807-818.
47. Topan, H., Taskanat, T., & Cam, A. (2013). Georeferencing accuracy assessment of Pléiades 1A images using rational function model. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W2.
48. Topan, H., & Maktav, D. (2014). Efficiency of orientation parameters on georeferencing accuracy of SPOT-5 HRG level-1A stereoimages. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3683-3694.
49. Topan, H., Oruc, M., Taskanat, T., & Cam, A. (2014). Combined efficiency of RPC and DEM accuracy on georeferencing accuracy of orthoimage: Case study with Pléiades panchromatic mono image. IEEE Geoscience and Remote Sensing Letters, 11(6), 1148-1152.
50. Waechter, M., Moehrle, N., & Goesele, M. (2014). Let there be color! Large-scale texturing of 3D reconstructions. European conference on computer vision, 836-850. Springer, Cham.
51. Wu, C. (2020). VisualSFM: A Visual Structure from Motion System. http://ccwu.me/vsfm/doc.html (Accessed: 30 August 2020).
52. Zoej, M. J. V. (1997). Photogrammetric Evaluation of Space Linear Array Imagery for Medium Scale Topographic Mapping (PhD Thesis). University of Glasgow, Faculty of Science, Glasgow, Scotland.