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Mevlana Türbesi Civarında Oluşan Kentsel Gelişim ve Değişimlerin Hava Fotogrametrisi Verilerinden Yararlanarak Görüntülenmesi

Yıl 2019, Cilt: 9 Sayı: 3, 433 - 443, 15.07.2019
https://doi.org/10.17714/gumusfenbil.463552

Öz

Fotogrametrik görüntülerden otomatik olarak tanımlanan yerel özellik
noktaları ile görüntüler arasında eşlenik noktalar oluşturulabilmekte ve
fotogrametrik bağıntılar yardımı ile istenilen sıklıkta nokta ölçüsü
gerçekleştirilebilmektedir. Fotoğraflardan elde edilen ölçü noktalarının
oluşturduğu nokta bulutu araziye ait zengin konum bilgisi içermektedir. Diğer
yandan ardışık nokta bulutu ölçülerinin karşılaştırılması ile görüntü alanına
ait değişiklikler tespit edilebilir. Bu çalışmada Konya ili Mevlana Türbesi
civarında oluşan kentsel değişimler incelenmiştir. 1951, 1975 ve 2010 yıllarına
ait fotogrametrik görüntülerden yoğun nokta bulutları oluşturulmuş ve nokta
bulutları arasındaki düşey farklar ile kentsel alan değişimleri
görüntülenmiştir. Ayrıca ölçü periyotlarına ait ortofoto görüntüler
oluşturularak değişimlerin görsel olarak değerlendirilebilmesi sağlanmıştır.

Kaynakça

  • Al-Rawabdeh, A., Moussa, A., Foroutan, M., El-Sheimy, N. ve Habib, A., 2017. Time series UAV image-based point clouds for land slide progression evaluation applications. Sensors, 17, paper no 2378 doi:10.3390/s17102378
  • Awrangjeba, M.,Fraser, C.S. ve Lua, G., 2015. Building change detection from LiDAR point cloud data based on connected component analysis. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-3/W5, 2015 ISPRS Geospatial Week 2015, 28 Sep – 03 Oct 2015, La Grande Motte, France, pp. 393-400.
  • Barnhart, T.B. ve Crosby, B.T., 2013. Comparing two methods of surface change detection on an evolving thermokarstusing high-temporal-frequency terrestrial laser scanning. Selawik River, Alaska. Remote Sensing, 5(6), 2813-2837. doi:10.3390/rs5062813
  • Basgall, P.L.,Kruse, F.A. ve Olsen, R.C., 2014. Comparison of LiDAR and stereo photogrammetric point clouds for change detection. Laser Radar Technology and Applications XIX; and Atmospheric Propagation XI, Editedby Monte D. Turner, Gary W. Kamerman, Linda M. Wasiczko Thomas, Earl J. Spillar, Proc. of SPIE Vol. 9080, 90800R, doi: 10.1117/12.2049856
  • Bildirici, I.O., Ustun, A., Selvi, H.Z., Abbak, R.A. ve Bugdayci, I., 2009. Assessment of shuttle radar topography mission elevation data based on topographic maps in Turkey. Cartography and Geographic Information Science, 36(1), 95-104.
  • Chen, Y. ve Medioni, G., 1992. Object modelling by registration of multiple range images. Image and Vision Computing, 10(3), 145–155.
  • Cusicanqui, J., 2016. 3D scenere construction and structural dmage assesment with aerial video frames and drone still imagery. Msc. Thesis, University of Twente, Enschede, The Nedherlands, 58 pages
  • Du, S., Zhang, Y., Qin, R., Yang, Z., Zou, Z. ve Tang, Y., 2016. Building change detection using old aerial images and new LiDAR data. Remote Sensing, 8(12), 1030, doi:10.3390/rs8121030
  • Ghuffar, S., Szekely, B., Roncat. A. ve Pfeifer, N., 2013. Land slide displacement monitoring using 3D range flow on airborne and terrestrial LiDAR data. Remote Sensing, 5, 2720-2745, doi:10.3390/rs5062720
  • Haala, N., 2011. Multiray photogrammetry and dense image matching. Photogrammetric Week 11, Dieter Fritsch (Ed.), Wichmann/VDE Verlag, Belin &Offenbach, pp. 185-195.
  • Hughes, M.L., McDowell, P.F. ve Marcus, W.A., 2006. Accuracy assessment of georectified aerial photographs: Implications for measuring lateral channel movement in a GIS. Geomorphology, 74(1-4), 1 –16.
  • Jensen, J.L.R. ve Mathews, A.J., 2016. Assessment of image-based point cloud products to generate a bare earth surface and estimate canopy heights in a woodland ecosystem. Remote Sensing, 8(1), 50, doi:10.3390/rs8010050
  • Leberl, F.,Irschara, A., Pock, T., Meixner, P., Gruber, M., Scholz, S. ve Wiechert, A., 2010. Point clouds: Lidar versus 3D vision. Photogrammetric Engineering & Remote Sensing, 76(10), 1123–1134.
  • Nebiker, S., Lack, N. ve Deuber, M., 2014. Building change detection from historical aerial photographs using dense image matching and object-based image analysis. Remote Sensing, 6(9), 8310-8336, doi:10.3390/rs6098310
  • Pang, S., Hu, X., Cai, Z., Gong, J. ve Zhang, M., 2018. Building change detection from bi-temporal dense-matching point clouds and aerial images. Sensors, 2018, 18, 966; doi:10.3390/s18040966
  • Rosnell, T., ve Honkavaara, E., 2012. Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera. Sensors, 12, 453-480, doi:10.3390/s120100453
  • Scaioni, M., Roncella, R. ve Alba, M.I., 2013. Change detection and deformation analysis in point clouds: Application to rock face monitoring. Photogrammetric Engineering and Remote Sensing, 79(5), 441-455.
  • Sisto, D.A. ve Packalen, P., 2017. Forest change detection by using point clouds from dense image matching together with a LiDAR-derived terrain model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(3), 1197-1206.
  • Tran, T.H.G., Ressl, C. ve Pfeifer, N., 2018. Integrated change detection and classification in urban areas based on airborne laser scanning point clouds. Sensors, 18(2), paper no 448, doi:10.3390/s18020448
  • Xiao, W.,Vallet, B., Brédif, M. ve Paparoditis, N., 2015. Street environment change detection from mobile laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 107, 38-49.
  • Yang, M.D., Chao, C.F., Huang, K.S., Lu, L.Y. ve Chen, Y.P., 2013. Image-based 3D reconstruction and exploration in augmented reality. Automation in Construction, 33, 48-60.
  • Zhang, X., Glennie, C. ve Kusari, A., 2015. Change detection from differential airborne LiDAR using a weighted anisotropic iterative closest point algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7), 3338-3346.

Urban Growing and Change Visualization in Mevlana Region Using Spatial Data from Aerial Images

Yıl 2019, Cilt: 9 Sayı: 3, 433 - 443, 15.07.2019
https://doi.org/10.17714/gumusfenbil.463552

Öz

Keypoints which is
detected automatically from images enable conjugate points creation between
photogrammetric images, and dense point cloud can be generated by proceeding
the photogrammetric process. The dense point cloud data includes many spatial
information related to imaging area. On the other hand topographic changes can
be detected by comparing two periods of point clouds. In this study urban
changes in Mevlana region of Konya city was visualized by comparing three
periods of point clouds belong the year 1951, 1975 and 2010. The urban changes
were estimated with the vertical distances between compared point clouds. In
addition, orthophoto images were created for analysing the related changes.

Kaynakça

  • Al-Rawabdeh, A., Moussa, A., Foroutan, M., El-Sheimy, N. ve Habib, A., 2017. Time series UAV image-based point clouds for land slide progression evaluation applications. Sensors, 17, paper no 2378 doi:10.3390/s17102378
  • Awrangjeba, M.,Fraser, C.S. ve Lua, G., 2015. Building change detection from LiDAR point cloud data based on connected component analysis. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-3/W5, 2015 ISPRS Geospatial Week 2015, 28 Sep – 03 Oct 2015, La Grande Motte, France, pp. 393-400.
  • Barnhart, T.B. ve Crosby, B.T., 2013. Comparing two methods of surface change detection on an evolving thermokarstusing high-temporal-frequency terrestrial laser scanning. Selawik River, Alaska. Remote Sensing, 5(6), 2813-2837. doi:10.3390/rs5062813
  • Basgall, P.L.,Kruse, F.A. ve Olsen, R.C., 2014. Comparison of LiDAR and stereo photogrammetric point clouds for change detection. Laser Radar Technology and Applications XIX; and Atmospheric Propagation XI, Editedby Monte D. Turner, Gary W. Kamerman, Linda M. Wasiczko Thomas, Earl J. Spillar, Proc. of SPIE Vol. 9080, 90800R, doi: 10.1117/12.2049856
  • Bildirici, I.O., Ustun, A., Selvi, H.Z., Abbak, R.A. ve Bugdayci, I., 2009. Assessment of shuttle radar topography mission elevation data based on topographic maps in Turkey. Cartography and Geographic Information Science, 36(1), 95-104.
  • Chen, Y. ve Medioni, G., 1992. Object modelling by registration of multiple range images. Image and Vision Computing, 10(3), 145–155.
  • Cusicanqui, J., 2016. 3D scenere construction and structural dmage assesment with aerial video frames and drone still imagery. Msc. Thesis, University of Twente, Enschede, The Nedherlands, 58 pages
  • Du, S., Zhang, Y., Qin, R., Yang, Z., Zou, Z. ve Tang, Y., 2016. Building change detection using old aerial images and new LiDAR data. Remote Sensing, 8(12), 1030, doi:10.3390/rs8121030
  • Ghuffar, S., Szekely, B., Roncat. A. ve Pfeifer, N., 2013. Land slide displacement monitoring using 3D range flow on airborne and terrestrial LiDAR data. Remote Sensing, 5, 2720-2745, doi:10.3390/rs5062720
  • Haala, N., 2011. Multiray photogrammetry and dense image matching. Photogrammetric Week 11, Dieter Fritsch (Ed.), Wichmann/VDE Verlag, Belin &Offenbach, pp. 185-195.
  • Hughes, M.L., McDowell, P.F. ve Marcus, W.A., 2006. Accuracy assessment of georectified aerial photographs: Implications for measuring lateral channel movement in a GIS. Geomorphology, 74(1-4), 1 –16.
  • Jensen, J.L.R. ve Mathews, A.J., 2016. Assessment of image-based point cloud products to generate a bare earth surface and estimate canopy heights in a woodland ecosystem. Remote Sensing, 8(1), 50, doi:10.3390/rs8010050
  • Leberl, F.,Irschara, A., Pock, T., Meixner, P., Gruber, M., Scholz, S. ve Wiechert, A., 2010. Point clouds: Lidar versus 3D vision. Photogrammetric Engineering & Remote Sensing, 76(10), 1123–1134.
  • Nebiker, S., Lack, N. ve Deuber, M., 2014. Building change detection from historical aerial photographs using dense image matching and object-based image analysis. Remote Sensing, 6(9), 8310-8336, doi:10.3390/rs6098310
  • Pang, S., Hu, X., Cai, Z., Gong, J. ve Zhang, M., 2018. Building change detection from bi-temporal dense-matching point clouds and aerial images. Sensors, 2018, 18, 966; doi:10.3390/s18040966
  • Rosnell, T., ve Honkavaara, E., 2012. Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera. Sensors, 12, 453-480, doi:10.3390/s120100453
  • Scaioni, M., Roncella, R. ve Alba, M.I., 2013. Change detection and deformation analysis in point clouds: Application to rock face monitoring. Photogrammetric Engineering and Remote Sensing, 79(5), 441-455.
  • Sisto, D.A. ve Packalen, P., 2017. Forest change detection by using point clouds from dense image matching together with a LiDAR-derived terrain model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(3), 1197-1206.
  • Tran, T.H.G., Ressl, C. ve Pfeifer, N., 2018. Integrated change detection and classification in urban areas based on airborne laser scanning point clouds. Sensors, 18(2), paper no 448, doi:10.3390/s18020448
  • Xiao, W.,Vallet, B., Brédif, M. ve Paparoditis, N., 2015. Street environment change detection from mobile laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 107, 38-49.
  • Yang, M.D., Chao, C.F., Huang, K.S., Lu, L.Y. ve Chen, Y.P., 2013. Image-based 3D reconstruction and exploration in augmented reality. Automation in Construction, 33, 48-60.
  • Zhang, X., Glennie, C. ve Kusari, A., 2015. Change detection from differential airborne LiDAR using a weighted anisotropic iterative closest point algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7), 3338-3346.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Cihan Altuntaş 0000-0002-5754-2068

Yayımlanma Tarihi 15 Temmuz 2019
Gönderilme Tarihi 25 Eylül 2018
Kabul Tarihi 18 Şubat 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 9 Sayı: 3

Kaynak Göster

APA Altuntaş, C. (2019). Mevlana Türbesi Civarında Oluşan Kentsel Gelişim ve Değişimlerin Hava Fotogrametrisi Verilerinden Yararlanarak Görüntülenmesi. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 9(3), 433-443. https://doi.org/10.17714/gumusfenbil.463552