Urban area change visualization and analysis using high density spatial data from time series aerial images

• Autorzy: Altuntas Cihan

Identyfikatory

DOI

10.2478/rgg-2019-0001

• Języki publikacji: EN

Abstrakty

EN

Urban changes occur as a result of new constructions or destructions of buildings, extensions, excavation works and earth fill arising from urbanization or disasters. The fast and efficient detection of urban changes enables us to update geo-databases and allows effective planning and disaster management. This study concerns the visualization and analysis of urban changes using multi-period point clouds from aerial images. The urban changes in the city centre of the Konya Metropolitan area within arbitrary periods between the years 1951, 1975, 1998 and 2010 were estimated after comparing the point clouds by using the iterative closest point (ICP) algorithm. The changes were detected with the point-to-surface distances between the point clouds. The degrees of the changes were expressed with the RMSEs of these point-to-surface distances. In addition, the change size and proportion during the historical periods were analysed. The proposed multi-period change visualization and analysis method ensures strict management against unauthorized building or excavation and more operative urban planning.

Słowa kluczowe

EN

photogrammetry; aerial image; image-based point cloud; digital elevation model; visualization of changes; urban area

PL

fotogrametria; zdjęcie lotnicze; chmura punktów; cyfrowy model wysokościowy; wizualizacja zmian; przestrzeń miejska

• Wydawca: Wydział Geodezji i Kartografii Politechniki Warszawskiej
• Czasopismo: Reports on Geodesy and Geoinformatics
• Rocznik: 2019
• Tom: Vol. 107
• Strony: 1--12
• Opis fizyczny: Bibliogr. 55 poz., tab., rys., wykr.

Twórcy

autor

Altuntas Cihan

• Department of Geomatics, Engineering Faculty, Selçuk University, Alaaddin Keykubat Campus, 42075, Selcuklu/Konya, Turkey

Bibliografia

• [1] Agisoft (2017). Photoscan Professional Version 1.3.2.4205.
• [2] Ahmadabadian A. H. Robson S. Boehm J. and Shortis M. (2014). Stereo-imaging network design for preciseand dense 3D reconstruction. The Photogrammetric Record 29(147):317–336
• [3] Ahmadabadian A. H. Robson S. Boehm J. Shortis M. Wenzel K. and Fritsch D. (2013). A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs. ISPRS Journal of Photogrammetry and Remote Sensing 78:157–167
• [4] Aicardi I. Chiabrando F. Lingua A. M. and Noardo F. (2018). Recent trends in cultural heritage 3D survey: The photogrammetric computer vision approach. Journal of Cultural Heritage 32:257–266
• [5] Al-Rawabdeh A. Moussa A. Foroutan M. El-Sheimy N. and Habib A. (2017). Time series UAV image-based point clouds for landslide progression evaluation applications. Sensors 17(10):2378
• [6] Ali-Sisto D. and 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
• [7] Alsadik B. Gerke M. and Vosselman G. (2013). Automated camera network design for 3D modeling of cultural heritage objects. Journal of Cultural Heritage 14(6):515–526
• [8] Altan O. Toz G. Kulur S. Seker D. Volz S. Fritsch D. and Sester M. (2001). Photogrammetry and geographic information systems for quick assessment documentation and analysis of earthquakes. ISPRS Journal of Photogrammetry and Remote Sensing 55(5-6):359–372
• [9] Altuntas C. (2013). Keypoint based automatic image orientation and skew investigation on tie points. Kybernetes 42(3):506–520
• [10] Altuntas C. (2014). The effect of point density on the registration accuracy of a terrestrial laser scanning dataset. Lasers in Engineering 28(3-4):213–221.
• [11] Awrangjeb M. Fraser C. S. and Lu 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 II-3/W5:393–400
• [12] Barazzetti L. Scaioni M. and Remondino F. (2010). Orientation and 3D modelling from markerless terrestrial images: combining accuracy with automation. The Photogrammetric Record 25(132):356–381
• [13] Barnhart T. and Crosby B. (2013). Comparing two methods of surface change detection on an evolving thermokarst using high-temporal-frequency terrestrial laser scanning Selawik River Alaska. Remote Sensing 5(6):2813–2837
• [14] Basgall P. L. Kruse F. A. and Olsen R. C. (2014). Comparison of LIDAR and stereo photogrammetric point clouds for change detection. In Laser Radar Technology and Applications XIX; and Atmospheric Propagation XI volume 9080R. International Society for Optics and Photonics.
• [15] Bay H. Tuytelaars T. and Van Gool L. (2006). SURF: Speeded up robust features. In Leonardis A. Bischof H. and Pinz A. editors Computer Vision – ECCV 2006 pages 404–417. Springer.
• [16] Besl P. J. and McKay N. D. (1992). Method for registration of 3-D shapes. In Sensor Fusion IV: Control Paradigms and Data Structures volume 1611 pages 586–607. International Society for Optics and Photonics.
• [17] Bildirici O. I. Ustun A. Selvi Z. H. Abbak A. R. and 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
• [18] Calonder M. Lepetit V. Strecha C. and Fua P. (2010). BRIEF: Binary robust independent elementary features. In Daniilidis K. Maragos P. and Paragios N. editors "Computer Vision – ECCV 2010 pages 778–792. Springer.
• [19] Chen B. Chen Z. Deng L. Duan Y. and Zhou J. (2016). Building change detection with RGB-D map generated from UAV images. Neurocomputing 208:350–364
• [20] Cooper M. A. R. and Robson S. (1990). High precision photogrammetric monitoring of the deformation of a steel bridge. The Photogrammetric Record 13(76):505–510
• [21] Cusicanqui J. (2016). 3D scene reconstruction and structural damage assessment with aerial video frames and drone still imagery. Master’s thesis University of Twente.
• [22] Di K. Xu F. Wang J. Agarwal S. Brodyagina E. Li R. and Matthies L. (2008). Photogrammetric processing of rover imagery of the 2003 Mars Exploration Rover mission. ISPRS Journal of Photogrammetry and Remote Sensing 63(2):181–201
• [23] Du S. Zhang Y. Qin R. Yang Z. Zou Z. Tang Y. and Fan C. (2016). Building change detection using old aerial images and new LIDAR data. Remote Sensing 8(12):1030
• [24] Fraser C. Hanley H. and Cronk S. (2005). Close-range photogrammetry for accident reconstruction. In Gruen A. and Kahmen H. editors Optical 3D Measurements VII volume II pages 115–123.
• [25] Gabrlik P. (2015). The use of direct georeferencing in aerial photogrammetry with micro UAV. IFAC-PapersOnLine 48(4):380–385
• [26] Ghuffar S. Székely B. Roncat A. and Pfeifer N. (2013). Landslide displacement monitoring using 3D range flow on airborne and terrestrial LIDAR data. Remote Sensing 5(6):2720–2745
• [27] Haala N. (2011). Multiray photogrammetry and dense image matching. In Fritsch D. editor Photogrammetric Week volume 11 pages 185–195.
• [28] Haala N. and Rothermel M. (2012). Dense multiple stereo matching of highly overlapping UAV imagery. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences XXXIX-B1:387–392
• [29] Hartley R. and Zisserman A. (2003). Multiple view geometry in computer vision Cambridge University Press.
• [30] Hebel M. Arens M. and Stilla U. (2013). Change detection in urban areas by object-based analysis and on-the-fly comparison of multi-view ALS data. ISPRS Journal of Photogrammetry and Remote Sensing 86:52–64
• [31] Honkavaara E. Markelin L. Rosnell T. and Nurminen K. (2012). Influence of solar elevation in radiometric and geometric performance of multispectral photogrammetry. IS-PRS Journal of Photogrammetry and Remote Sensing 67:13–26
• [32] Hughes M. L. McDowell P. F. and 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
• [33] James M. R. Robson S. and Smith M. W. (2017). 3-D uncertainty-based topographic change detection with structure-from-motion photogrammetry: precision maps for ground control and directly georeferenced surveys. Earth Surface Processes and Landforms 42(12):1769–1788
• [34] Jensen J. and Mathews A. (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
• [35] Jiang R. Jáuregui D. V. and White K. R. (2008). Close-range photogrammetry applications in bridge measurement: literature review. Measurement 41(8):823–834
• [36] Leberl F. Irschara A. Pock T. Meixner P. Gruber M. Scholz S. and Wiechert A. (2010). Point clouds: LIDAR versus 3D vision. Photogrammetric Engineering & Remote Sensing 76(10):1123–1134
• [37] Lingua A. Piumatti P. and Rinaudo F. (2003). Digital photogrammetry: a standard approach to cultural heritage survey. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences 34(5/W12):210–215.
• [38] Lovitt J. Rahman M. M. and McDermid G. J. (2017). Assessing the value of UAV photogrammetry for characterizing terrain in complex peatlands. Remote Sensing 9(7):715
• [39] Lowe D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision 60(2):91–110
• [40] Mora O. E. Lenzano M. G. Toth C. K. Grejner-Brzezinska D. and Fayne J. V. (2018). Landslide change detection based on multi-temporal airborne LIDAR-derived DEMs. Geo-sciences 8(1):23
• [41] Nebiker S. Lack N. and 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
• [42] Pang S. Hu X. Cai Z. Gong J. and Zhang M. (2018). Building change detection from bi-temporal dense-matching point clouds and aerial images. Sensors 18(4):966
• [43] Pfeifer N. Glira P. and Briese C. (2012). Direct georeferencing with on board navigation components of light weight UAV platforms. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences 39(B7):487–492
• [44] Remondino F. Spera M. G. Nocerino E. Menna F. Nex F. and Gonizzi-Barsanti S. (2013). Dense image matching: comparisons and analyses. In 2013 Digital Heritage International Congress (DigitalHeritage) volume 1 pages 47–54. IEEE.
• [45] Rosnell T. and 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(1):453–480
• [46] Rossi P. Mancini F. Dubbini M. Mazzone F. and Capra A. (2017). Combining nadir and oblique UAV imagery to reconstruct quarry topography: Methodology and feasibility analysis. European Journal of Remote Sensing 50(1):211–221
• [47] Scaioni M. Roncella R. and Alba M. I. (2013). Change detection and deformation analysis in point clouds: Application to rock face monitoring. Photogrammetric Engineering & Remote Sensing 79(5):441–455
• [48] Stumpf A. Malet J.-P. Allemand P. Pierrot-Deseilligny M. and Skupinski G. (2015). Ground-based multi-view photogrammetry for the monitoring of landslide deformation and erosion. Geomorphology 231:130–145
• [49] Tran T. H. G. Ressl C. and Pfeifer N. (2018). Integrated change detection and classification in urban areas based on airborne laser scanning point clouds. Sensors 18(2):448
• [50] Vu T. T. Matsuoka M. and Yamazaki F. (2004). LIDAR-based change detection of buildings in dense urban areas. In IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium volume 5 pages 3413–3416. IEEE.
• [51] Xiao W. Vallet B. Brédif M. and Paparoditis N. (2015). Street environment change detection from mobile laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing 107:38–49
• [52] Yang M.-D. Chao C.-F. Huang K.-S. Lu L.-Y. and Chen Y.-P. (2013). Image-based 3D scene reconstruction and exploration in augmented reality. Automation in Construction 33:48–60
• [53] Yu G. and Morel J.-M. (2011). ASIFT: An algorithm for fully affine invariant comparison. Image Processing On Line 1:11–38
• [54] Zhang X. Glennie C. and 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
• [55] Zhang Z. Gerke M. Vosselman G. and Yang M. Y. (2018). A patch-based method for the evaluation of dense image matching quality. International journal of applied earth observation and geoinformation 70:25–34

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).