Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2022, Cilt: 38 Sayı: 2, 201 - 217, 23.08.2022

Hacı Mustafa Palancıoglu

Öz

Destekleyen Kurum

-

Proje Numarası

-

Teşekkür

-

Kaynakça

  • [1] Westoby, M.J., Brasington, J., Glasser, N.F., Reynolds, J.M. 2012. ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179: 300-314.
  • [2] Mancini, F., Dubbini, M., Gattelli, M., et all.2013. Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments. Remote Sensing. 5, 12, 6880-6898.
  • [3] Nam, S., H, Ahn. W., et al. 2019. Content-Aware Image Resizing Detection Using Deep Neural Network. 26th IEEE International Conference on Image Processing (ICIP). 106-110.
  • [4] Liu, C.Y., Chang, C.C., et al. 2019. Image resizing using fuzzy inferences. IET Image Processing, 13, 12, 2058-2066.
  • [5] Lau, C.P., Yung, C.P., Lui, L.M. 2018. Image Retargeting via Beltrami Representation. IEEE Transactions On Image Processing, 27, 5787-5801.
  • [6] Xu, H.F., Yu, S.Y., Wang, C. 2017. An adaptive image resizing algorithm in DCT domain. IEICE Transactions On Information And Systems. E90D(8), 1308-1311.
  • [7] Park, Y.S., Lee, Y.L., Park, H.W. 2004. Image downsampling for interlaced-to-progressive transcoding in discrete cosine transform domain. Optical Engineering. 43, 9, 2100-2104.
  • [8] Trentacoste, M., Mantiuk, R., Heidrich, W. 2011. Blur-Aware Image Downsampling. Computer Graphics Forum. 30, 2, 573-582.
  • [9] Wang, Y.F., Wang, L.J., et al. 2018. Information-Compensated Downsampling for Image Super-Resolution. IEEE Signal Processing Letters, 25, 5, 685-689.
  • [10] Fang, L., Au, O.C., et al. 2012. Antialiasing Filter Design for Subpixel Downsampling via Frequency-Domain Analysis. IEEE Transactions on Image Processing, 21, 3, 1391-1405.
  • [11] Huang, F., Yang, H., Tao, J., et al. 2021. Preliminary study on the automatic parallelism optimization model for image enhancement algorithms based on Intel's (R) Xeon Phi. Concurr Comput Pract Exper. 33, 16, e6260. doi:10.1002/cpe.6260.
  • [12] Huang, F. 202. Research on the parallelization implementation of multi scale retinex image-enhancement algorithm based a MIC platform. Concurr Comput Pract Exper. 32, 22, e5832. doi:10.1002/cpe.5832.
  • [13] Lee, J., Sung, S. 2016. Evaluating spatial resolution for quality assurance of UAV images. Spatial Information Research. 24, 2, 141-154.
  • [14] Park, S., Lee, H., Chon, J. 2019. Sustainable monitoring coverage of unmanned aerial vehicle photogrammetry according to wing type and image resolution. Environmental Pollution. 247, 340-348.
  • [15] Lee, J., Sung, S. 2016. Evaluating spatial resolution for quality assurance of UAV images. Spatial Information Research. 24, 2, 141-154.
  • [16] Ajibola, I.I., Mansor, S., Pradhan, B., et al. 2019. Fusion of UAV-based DEMs for vertical component accuracy improvement. Measurement. 147 , 106795.
  • [17] Nesbit, P.R., Hugenholtz, C.H. 2019. Enhancing UAV-SfM 3D Model Accuracy in High-Relief Landscapes by Incorporating Oblique Images. Remote Sensing. 11, 3, 239. DOI:10.3390/rs11030239.
  • [18] Akar, A. 2017. Evaluation Of Accuracy of Dems Obtained From UAV-Point Clouds For Different Topographical Areas. International Journal of Engineering and Geosciences. 2 , 3, 110-117.
  • [19] Tomastik, J., Mokros, M. 2017. Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy. Forests. 8, 5, 151. DOI:10.3390/f8050151.
  • [20] Hlotov, V., Hunina, A., Siejka, Z. 2017. Accuracy Investigation Of Creating Orthophotomaps Based On Images Obtained By Applying Trimble-Ux5 Uav. Reports On Geodesy And Geoinformatics. 103, 1, 106-118.
  • [21] Kucharczyk, M., Hugenholtz, C.H., Zou, X.Y. 2018. UAV-LiDAR accuracy in vegetated terrain. Journal Of Unmanned Vehicle Systems. 6, 4, 212-234.
  • [22] Cawood, A.J., Bond, C.E., et al. 2017. LiDAR, UAV or compass-clinometer? Accuracy, coverage and the effects on structural models. Journal Of Structural Geology. 98 : 67-82.
  • [23] Martinez-Carricondo, P., Aguera-Vega, F., et al. 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.
  • [24] Jeong, E., Park, J.Y., Hwang, C.S. 2018. Assessment of UAV Photogrammetric Mapping Accuracy in the Beach Environment. Journal Of Coastal Research. 85, 176-180. Doi:10.2112/SI85-036.1.
  • [25] Uysal, M., Toprak, A.S., Polat, N. 2015. DEM generation with UAV Photogrammetry and accuracy analysis in Sahitler hill. Measurement. 73 :539-543.
  • [26] Wiseman, DJ and van der Sluijs, J. 2015. Alternative Methods for Developing and Assessing the Accuracy of UAV-Derived DEMs. International Journal Of Applied Geospatial Research. 6 , 3, 58-77.
  • [27] Cesnulevicius, A., Bautrenas, A., et al. 2019. Comparison of Accuracy of UAV Aerials and Ground Measurements in the Curonian Spit Dunes. Baltic Journal Of Modern Computing. 7 , 4, 571-585.
  • [28] Huang, F., Lan, B., Tao, J., et al. 2017. A parallel nonlocal means algorithm for remote sensing image denoising on an Intel Xeon Phi platform. IEEE Access. 5 , 8559-8567.
  • [29] Zhang, X.L., Dai, L.Q. 2019. Fast bilateral filtering. Electronics Letters. 55, 5, 258-259.
  • [30] Tomastik, J., Mokros, M., et al. 2019. UAV RTK/PPK Method An Optimal Solution for Mapping Inaccessible Forested Areas?. Remote Sensing. 11, 6, 721. DOI10.3390/rs11060721.
  • [31] Turner, D., Lucieer, A., Wallace, L. 2014. Direct Georeferencing of Ultrahigh-Resolution UAV Imagery. IEEE Transactions On Geoscience And Remote Sensing. 52 , 5, 2738-2745.
  • [32] Ekaso, D., Nex, F., Kerle, N. 2020. Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing. Geo-Spatial Information Science. 23 , 2, 165-181.
  • [33] Stroner, M., Urban, R., Seidl, J., et al. 2021. Photogrammetry Using UAV-Mounted GNSS RTK: Georeferencing Strategies without GCPs. Remote Sensing. 13, 7, 1336. DOI10.3390/rs13071336.
  • [34] Forlani, G., Dall'Asta, E., et al. 2018. Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning. Remote Sensing. 10, 2, 311. DOI10.3390/rs10020311.
  • [35] Chen, B.H., Tseng, Y.S., Yin, J.L. 2020. Gaussian - Adaptive Bilateral Filter. IEEE Signal Processing Letters. 27, 1670 -1674.
  • [36] Das, S., Mullick, S.S., Suganthan, P.N. 2016. Recent advances in differential evolution - An updated survey. Swarm And Evolutionary Computation. 27 : 1-30.
  • [37] Yeo, C.H., Tan, H.L., Tan, Y.H. 2013. On Rate Distortion Optimization Using SSIM. IEEE Transactions on Circuits And Systems For Video Technology. 23 , 7, 1170-1181.
  • [38] Rukundo, O., Schmidt, S.E. 2018. Effects of rescaling bilinear interpolant on image interpolation quality. Optoelectronic Imaging And Multimedia Technology V. Proceedings of SPIE. 10817. UNSP 1081715. DOI:10.1117/12.2501549.
  • [39] Jini, P., Kumar, K.R. 2021. Image Inpainting Using Image Interpolation - An Analysis. Revista Geintec-Gestao Inovacao E Tecnologias. 11 ,2, 1906-1920.
  • [40] Zhang, L.Z., Zhang, W., et al. 2021. Feature-level interpolation-based GAN for image super-resolution. Personal And Ubiquitous Computing. Early Access. DOI:10.1007/s00779-020-01488-y.
  • [41] Jin, J.G. 2020. An Adaptive Image Scaling Algorithm Based On Continuous Fraction Interpolation And Multi-Resolution Hierarchy Processing. Fractals-Complex Geometry Patterns And Scaling In Nature And Society. 28, 8, 2040016, DOI:10.1142/S0218348X20400162.
  • [42] Wang, P., Yao, H.Y., Zhang, G. 2021. A novel interpolation-based subpixel mapping for hyperspectral image by using pansharpening. Journal Of Infrared And Millimeter Waves. 40, 1, 56-63.
  • [43] Won, C.S., Jung, S.W. 2017. Near-reversible efficient image resizing for devices supporting different spatial resolutions. Journal Of Supercomputing. 73, 7, 3021-3037.
  • [44] Arar, M., Danon, D., Cohen-Or, D., Shamir, A. 2021. Image resizing by reconstruction from deep features. Computational Visual Media. 7 , 4, 453-466.
  • [45] Yan, B., Tan, W.M., et al. 2017. Codebook Guided Feature-Preserving for Recognition-Oriented Image Retargeting. IEEE Transactions on Image Processing. 26, 5, 2454-2465.
  • [46] Khan, M.U., Baig, M.A., Moinuddin, A.A. 2017. Full Reference Quality Assessment of Downsized Images. International Conference on Multimedia, Signal Processing and Communication Technologies (Impact). 271-274.
  • [47] Wang, Q., Yuan, Y. 2014. High quality image resizing. Neurocomputing. 131: 348-356.
  • [48] Danon, D., Arar, M., et al. 2021. Image resizing by reconstruction from deep features. Computational Visual Media. 7, 4, 453-466.
  • [49] Trajkovski, K.K., Grigillo, D., Petrovic, D. 2020. Optimization of UAV Flight Missions in Steep Terrain. Remote Sensing. 12, 8, Article Number 1293 .
  • [50] Sadeq H. A. 2019. Accuracy assessment using different UAV image overlaps. Journal Of Unmanned Vehicle Systems. 7, 3, 175-193.
  • [51] Torres-Sanchez, J., Lopez-Granados, F., et al. 2018. Assessing UAV-collected image overlap influence on computation time and digital surface model accuracy in olive orchards. Precision Agriculture. 19, 1, 115-133.
  • [52] Meshroom Software . https://github.com/alicevision/meshroom (last access 30.11.2021)
  • [53] 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 , Article Number 1885.
  • [54] CloudCompare Software . https://www.danielgm.net/cc/ (last access 30.11.2021)
  • [55] Jimenez-Jimenez, S.I., Ojeda-Bustamante, W., et al. 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.
  • [56] Ferrer-Gonzalez, E., Aguera-Vega, F., et al. 2020. UAV Photogrammetry Accuracy Assessment for Corridor Mapping Based on the Number and Distribution of Ground Control Points. Remote Sensing. 12 , 15 , 2447.DOI10.3390/rs12152447.
  • [57] RTKLIB Software. http://www.rtklib.com/ (last access 30.11.2021)
  • [58] UBLOX GNSS Sensors. https://www.u-blox.com/en (last access 30.11.2021)

Evolutionary Image Resizing based Accuracy Optimization for Aerial Triangulation

Yıl 2022, Cilt: 38 Sayı: 2, 201 - 217, 23.08.2022

Hacı Mustafa Palancıoglu

Öz

Flight altitude and properties of the imaging-sensor influences spatial-resolution of aerial-images used for SfM photogrammetry. Most of the mini-UAVs developed for civilian use can provide 8-bit, 12-64 MP RGB aerial images with GNSS-geotags. As the number of aerial images obtained from related UAVs increases, various computational difficulties are encountered due to practical reasons for photogrammetric aerial triangulation process. Image Resizing is a common operation in SfM Photogrammetry for downsampling the aerial images. By using Image Resizing methods, images that are downsampled to different levels can make it easier to produce data that meets the desired accuracy criteria from aerial mapping. In order not to cause excessive loss of detail in the image produced at the end of the Image Resizing operation, the high-frequency data is partially suppressed by softening the relevant image before it is reduced. Over-smoothing the image or over-suppressing high-frequency data may cause altered, distorted, or lost details. Therefore, it is difficult to implement the Image Resizing operation in an ideal way. In this paper, Bilateral Filter is used to smooth the related aerial images for Image Resizing. The best values of the internal parameters of the respective Bilateral Filter were calculated using the Differential Evolution Algorithm, DE. The statistical spatial-accuracy values computed for the photogrammetric products obtained by using resized images with DE-based Bilateral Filter exposed that the related products can met the spatial-accuracy standards for various engineering applications.

Anahtar Kelimeler

Aerial-imaging Multiscale DTM Image Downsampling Spatial Accuracy SfM Photogrammetry

Proje Numarası

-

Kaynakça

  • [1] Westoby, M.J., Brasington, J., Glasser, N.F., Reynolds, J.M. 2012. ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179: 300-314.
  • [2] Mancini, F., Dubbini, M., Gattelli, M., et all.2013. Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments. Remote Sensing. 5, 12, 6880-6898.
  • [3] Nam, S., H, Ahn. W., et al. 2019. Content-Aware Image Resizing Detection Using Deep Neural Network. 26th IEEE International Conference on Image Processing (ICIP). 106-110.
  • [4] Liu, C.Y., Chang, C.C., et al. 2019. Image resizing using fuzzy inferences. IET Image Processing, 13, 12, 2058-2066.
  • [5] Lau, C.P., Yung, C.P., Lui, L.M. 2018. Image Retargeting via Beltrami Representation. IEEE Transactions On Image Processing, 27, 5787-5801.
  • [6] Xu, H.F., Yu, S.Y., Wang, C. 2017. An adaptive image resizing algorithm in DCT domain. IEICE Transactions On Information And Systems. E90D(8), 1308-1311.
  • [7] Park, Y.S., Lee, Y.L., Park, H.W. 2004. Image downsampling for interlaced-to-progressive transcoding in discrete cosine transform domain. Optical Engineering. 43, 9, 2100-2104.
  • [8] Trentacoste, M., Mantiuk, R., Heidrich, W. 2011. Blur-Aware Image Downsampling. Computer Graphics Forum. 30, 2, 573-582.
  • [9] Wang, Y.F., Wang, L.J., et al. 2018. Information-Compensated Downsampling for Image Super-Resolution. IEEE Signal Processing Letters, 25, 5, 685-689.
  • [10] Fang, L., Au, O.C., et al. 2012. Antialiasing Filter Design for Subpixel Downsampling via Frequency-Domain Analysis. IEEE Transactions on Image Processing, 21, 3, 1391-1405.
  • [11] Huang, F., Yang, H., Tao, J., et al. 2021. Preliminary study on the automatic parallelism optimization model for image enhancement algorithms based on Intel's (R) Xeon Phi. Concurr Comput Pract Exper. 33, 16, e6260. doi:10.1002/cpe.6260.
  • [12] Huang, F. 202. Research on the parallelization implementation of multi scale retinex image-enhancement algorithm based a MIC platform. Concurr Comput Pract Exper. 32, 22, e5832. doi:10.1002/cpe.5832.
  • [13] Lee, J., Sung, S. 2016. Evaluating spatial resolution for quality assurance of UAV images. Spatial Information Research. 24, 2, 141-154.
  • [14] Park, S., Lee, H., Chon, J. 2019. Sustainable monitoring coverage of unmanned aerial vehicle photogrammetry according to wing type and image resolution. Environmental Pollution. 247, 340-348.
  • [15] Lee, J., Sung, S. 2016. Evaluating spatial resolution for quality assurance of UAV images. Spatial Information Research. 24, 2, 141-154.
  • [16] Ajibola, I.I., Mansor, S., Pradhan, B., et al. 2019. Fusion of UAV-based DEMs for vertical component accuracy improvement. Measurement. 147 , 106795.
  • [17] Nesbit, P.R., Hugenholtz, C.H. 2019. Enhancing UAV-SfM 3D Model Accuracy in High-Relief Landscapes by Incorporating Oblique Images. Remote Sensing. 11, 3, 239. DOI:10.3390/rs11030239.
  • [18] Akar, A. 2017. Evaluation Of Accuracy of Dems Obtained From UAV-Point Clouds For Different Topographical Areas. International Journal of Engineering and Geosciences. 2 , 3, 110-117.
  • [19] Tomastik, J., Mokros, M. 2017. Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy. Forests. 8, 5, 151. DOI:10.3390/f8050151.
  • [20] Hlotov, V., Hunina, A., Siejka, Z. 2017. Accuracy Investigation Of Creating Orthophotomaps Based On Images Obtained By Applying Trimble-Ux5 Uav. Reports On Geodesy And Geoinformatics. 103, 1, 106-118.
  • [21] Kucharczyk, M., Hugenholtz, C.H., Zou, X.Y. 2018. UAV-LiDAR accuracy in vegetated terrain. Journal Of Unmanned Vehicle Systems. 6, 4, 212-234.
  • [22] Cawood, A.J., Bond, C.E., et al. 2017. LiDAR, UAV or compass-clinometer? Accuracy, coverage and the effects on structural models. Journal Of Structural Geology. 98 : 67-82.
  • [23] Martinez-Carricondo, P., Aguera-Vega, F., et al. 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.
  • [24] Jeong, E., Park, J.Y., Hwang, C.S. 2018. Assessment of UAV Photogrammetric Mapping Accuracy in the Beach Environment. Journal Of Coastal Research. 85, 176-180. Doi:10.2112/SI85-036.1.
  • [25] Uysal, M., Toprak, A.S., Polat, N. 2015. DEM generation with UAV Photogrammetry and accuracy analysis in Sahitler hill. Measurement. 73 :539-543.
  • [26] Wiseman, DJ and van der Sluijs, J. 2015. Alternative Methods for Developing and Assessing the Accuracy of UAV-Derived DEMs. International Journal Of Applied Geospatial Research. 6 , 3, 58-77.
  • [27] Cesnulevicius, A., Bautrenas, A., et al. 2019. Comparison of Accuracy of UAV Aerials and Ground Measurements in the Curonian Spit Dunes. Baltic Journal Of Modern Computing. 7 , 4, 571-585.
  • [28] Huang, F., Lan, B., Tao, J., et al. 2017. A parallel nonlocal means algorithm for remote sensing image denoising on an Intel Xeon Phi platform. IEEE Access. 5 , 8559-8567.
  • [29] Zhang, X.L., Dai, L.Q. 2019. Fast bilateral filtering. Electronics Letters. 55, 5, 258-259.
  • [30] Tomastik, J., Mokros, M., et al. 2019. UAV RTK/PPK Method An Optimal Solution for Mapping Inaccessible Forested Areas?. Remote Sensing. 11, 6, 721. DOI10.3390/rs11060721.
  • [31] Turner, D., Lucieer, A., Wallace, L. 2014. Direct Georeferencing of Ultrahigh-Resolution UAV Imagery. IEEE Transactions On Geoscience And Remote Sensing. 52 , 5, 2738-2745.
  • [32] Ekaso, D., Nex, F., Kerle, N. 2020. Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing. Geo-Spatial Information Science. 23 , 2, 165-181.
  • [33] Stroner, M., Urban, R., Seidl, J., et al. 2021. Photogrammetry Using UAV-Mounted GNSS RTK: Georeferencing Strategies without GCPs. Remote Sensing. 13, 7, 1336. DOI10.3390/rs13071336.
  • [34] Forlani, G., Dall'Asta, E., et al. 2018. Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning. Remote Sensing. 10, 2, 311. DOI10.3390/rs10020311.
  • [35] Chen, B.H., Tseng, Y.S., Yin, J.L. 2020. Gaussian - Adaptive Bilateral Filter. IEEE Signal Processing Letters. 27, 1670 -1674.
  • [36] Das, S., Mullick, S.S., Suganthan, P.N. 2016. Recent advances in differential evolution - An updated survey. Swarm And Evolutionary Computation. 27 : 1-30.
  • [37] Yeo, C.H., Tan, H.L., Tan, Y.H. 2013. On Rate Distortion Optimization Using SSIM. IEEE Transactions on Circuits And Systems For Video Technology. 23 , 7, 1170-1181.
  • [38] Rukundo, O., Schmidt, S.E. 2018. Effects of rescaling bilinear interpolant on image interpolation quality. Optoelectronic Imaging And Multimedia Technology V. Proceedings of SPIE. 10817. UNSP 1081715. DOI:10.1117/12.2501549.
  • [39] Jini, P., Kumar, K.R. 2021. Image Inpainting Using Image Interpolation - An Analysis. Revista Geintec-Gestao Inovacao E Tecnologias. 11 ,2, 1906-1920.
  • [40] Zhang, L.Z., Zhang, W., et al. 2021. Feature-level interpolation-based GAN for image super-resolution. Personal And Ubiquitous Computing. Early Access. DOI:10.1007/s00779-020-01488-y.
  • [41] Jin, J.G. 2020. An Adaptive Image Scaling Algorithm Based On Continuous Fraction Interpolation And Multi-Resolution Hierarchy Processing. Fractals-Complex Geometry Patterns And Scaling In Nature And Society. 28, 8, 2040016, DOI:10.1142/S0218348X20400162.
  • [42] Wang, P., Yao, H.Y., Zhang, G. 2021. A novel interpolation-based subpixel mapping for hyperspectral image by using pansharpening. Journal Of Infrared And Millimeter Waves. 40, 1, 56-63.
  • [43] Won, C.S., Jung, S.W. 2017. Near-reversible efficient image resizing for devices supporting different spatial resolutions. Journal Of Supercomputing. 73, 7, 3021-3037.
  • [44] Arar, M., Danon, D., Cohen-Or, D., Shamir, A. 2021. Image resizing by reconstruction from deep features. Computational Visual Media. 7 , 4, 453-466.
  • [45] Yan, B., Tan, W.M., et al. 2017. Codebook Guided Feature-Preserving for Recognition-Oriented Image Retargeting. IEEE Transactions on Image Processing. 26, 5, 2454-2465.
  • [46] Khan, M.U., Baig, M.A., Moinuddin, A.A. 2017. Full Reference Quality Assessment of Downsized Images. International Conference on Multimedia, Signal Processing and Communication Technologies (Impact). 271-274.
  • [47] Wang, Q., Yuan, Y. 2014. High quality image resizing. Neurocomputing. 131: 348-356.
  • [48] Danon, D., Arar, M., et al. 2021. Image resizing by reconstruction from deep features. Computational Visual Media. 7, 4, 453-466.
  • [49] Trajkovski, K.K., Grigillo, D., Petrovic, D. 2020. Optimization of UAV Flight Missions in Steep Terrain. Remote Sensing. 12, 8, Article Number 1293 .
  • [50] Sadeq H. A. 2019. Accuracy assessment using different UAV image overlaps. Journal Of Unmanned Vehicle Systems. 7, 3, 175-193.
  • [51] Torres-Sanchez, J., Lopez-Granados, F., et al. 2018. Assessing UAV-collected image overlap influence on computation time and digital surface model accuracy in olive orchards. Precision Agriculture. 19, 1, 115-133.
  • [52] Meshroom Software . https://github.com/alicevision/meshroom (last access 30.11.2021)
  • [53] 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 , Article Number 1885.
  • [54] CloudCompare Software . https://www.danielgm.net/cc/ (last access 30.11.2021)
  • [55] Jimenez-Jimenez, S.I., Ojeda-Bustamante, W., et al. 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.
  • [56] Ferrer-Gonzalez, E., Aguera-Vega, F., et al. 2020. UAV Photogrammetry Accuracy Assessment for Corridor Mapping Based on the Number and Distribution of Ground Control Points. Remote Sensing. 12 , 15 , 2447.DOI10.3390/rs12152447.
  • [57] RTKLIB Software. http://www.rtklib.com/ (last access 30.11.2021)
  • [58] UBLOX GNSS Sensors. https://www.u-blox.com/en (last access 30.11.2021)
Toplam 58 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Hacı Mustafa Palancıoglu

Proje Numarası -
Erken Görünüm Tarihi 23 Ağustos 2022
Yayımlanma Tarihi 23 Ağustos 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 38 Sayı: 2

Kaynak Göster

APA Palancıoglu, H. M. (2022). Evolutionary Image Resizing based Accuracy Optimization for Aerial Triangulation. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 38(2), 201-217.
AMA Palancıoglu HM. Evolutionary Image Resizing based Accuracy Optimization for Aerial Triangulation. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. Ağustos 2022;38(2):201-217.
Chicago Palancıoglu, Hacı Mustafa. “Evolutionary Image Resizing Based Accuracy Optimization for Aerial Triangulation”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 38, sy. 2 (Ağustos 2022): 201-17.
EndNote Palancıoglu HM (01 Ağustos 2022) Evolutionary Image Resizing based Accuracy Optimization for Aerial Triangulation. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 38 2 201–217.
IEEE H. M. Palancıoglu, “Evolutionary Image Resizing based Accuracy Optimization for Aerial Triangulation”, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, c. 38, sy. 2, ss. 201–217, 2022.
ISNAD Palancıoglu, Hacı Mustafa. “Evolutionary Image Resizing Based Accuracy Optimization for Aerial Triangulation”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 38/2 (Ağustos 2022), 201-217.
JAMA Palancıoglu HM. Evolutionary Image Resizing based Accuracy Optimization for Aerial Triangulation. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2022;38:201–217.
MLA Palancıoglu, Hacı Mustafa. “Evolutionary Image Resizing Based Accuracy Optimization for Aerial Triangulation”. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, c. 38, sy. 2, 2022, ss. 201-17.
Vancouver Palancıoglu HM. Evolutionary Image Resizing based Accuracy Optimization for Aerial Triangulation. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2022;38(2):201-17.
 Kapak Resmi İndir

✯ Etik kurul izni gerektiren, tüm bilim dallarında yapılan araştırmalar için etik kurul onayı alınmış olmalı, bu onay makalede belirtilmeli ve belgelendirilmelidir.
✯ Etik kurul izni gerektiren araştırmalarda, izinle ilgili bilgilere (kurul adı, tarih ve sayı no) yöntem bölümünde, ayrıca makalenin ilk/son sayfalarından birinde; olgu sunumlarında, bilgilendirilmiş gönüllü olur/onam formunun imzalatıldığına dair bilgiye makalede yer verilmelidir.
✯ Dergi web sayfasında, makalelerde Araştırma ve Yayın Etiğine uyulduğuna dair ifadeye yer verilmelidir.
✯ Dergi web sayfasında, hakem, yazar ve editör için ayrı başlıklar altında etik kurallarla ilgili bilgi verilmelidir.
✯ Dergide ve/veya web sayfasında, ulusal ve uluslararası standartlara atıf yaparak, dergide ve/veya web sayfasında etik ilkeler ayrı başlık altında belirtilmelidir. Örneğin; dergilere gönderilen bilimsel yazılarda, ICMJE (International Committee of Medical Journal Editors) tavsiyeleri ile COPE (Committee on Publication Ethics)’un Editör ve Yazarlar için Uluslararası Standartları dikkate alınmalıdır.
✯ Kullanılan fikir ve sanat eserleri için telif hakları düzenlemelerine riayet edilmesi gerekmektedir.