Towards Automation of Information Extraction from Aerial and Satellite Images

  • John Trinder


Since photogrammetry was developed more than 100 years ago as a technology for map production and the measurement of objects on images, photogrammetrists have attempted to improve the efficiency and accuracy of the process. These attempts commenced with the development of analogue approaches to solving the major computations in converting image coordinates on photographs to coordinates in a ground or object system. Subsequent developments included computer driven instruments, the ‘analytical stereoplotter’ and in the early 1990s, purely digital systems based on digital image acquisition and processing. The precision of all components of the photogrammetric process was continually improved so that smaller image scales could be taken to achieve the required accuracy on the object, thus improving efficiency and reducing costs of the mapping process. Digital image processing in photogrammetry currently enables the determination of elevations and the production of digital orthophotos more rapidly and with greater efficiency than could be achieved with analogue instruments. However, while a certain level of automation has been achieved in the presentation of roads, buildings and other cultural features for the production of digital map data, the automatic extraction of these features from images has not been achieved. The availability of high resolution digital satellite and multispectral aerial images, coupled with the community’s increasing need for more detailed, timely and lower cost spatial information for the production of digital maps and GIS (Geographic Information System) databases, has driven research on feature extraction over recent decades. While this research continues to develop new approaches to the extraction of features from images, no system has been so far demonstrated that enables extraction of features reliably under a range of image conditions and scales.


Road Segment Data Fusion Multispectral Image Aerial Image Digital Surface Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Agouris P, Doucette P, Stefanidis A (2001) Spatiospectral cluster analysis of elongated regions in aerial imagery. IEEE Int. Conf. Image Proc 2:789–792Google Scholar
  2. Amini J, Sarahjian MR, (2000) Image map simplification by using mathematical morphology. ISPRS Int Arch Photogram Rem Sens 33(B3):36–47Google Scholar
  3. Bandyopadhyay S, Sowmya A, Maulik U (2000) Genetic classifier for extracting road segments. Proc Int Conf Communications Computers and Devices (II):455–458Google Scholar
  4. Barsi A, Heipke C, Willrich F (2002) Junction extraction by artificial neural network system genes, ISPRS Int Arch Photogram and Rem Sens & SIS 34(3B):18–21Google Scholar
  5. Baumgartner A, Hinz S, Wiedemann C (2002) Efficient methods, and interfaces for road tracking. Int Archives Photogram and Rem Sens & SIS 34(3B):28–31Google Scholar
  6. Baumgartner A, Steger C, Mayer H, Eckstein W, Ebner H (1999) Automatic Road Extraction Based on Mult-Scale, Grouping, and Context. Photogram Eng and Rem Sens 65 (7):777–785Google Scholar
  7. Benjamin S, Gaydos L (1990) Spatial resolution requirements for automated cartographic road extraction, Photogram Eng. Rem Sens & SIS 56(1):93–100Google Scholar
  8. Bonnefon R, Dherete P, Desachy J (2002) Geographic information system updating using remote sensing images. Pattern Recognition Letters 23(9):1073–1083CrossRefGoogle Scholar
  9. Brazdil PB, Henery RJ (1994) Analysis of results. In Michie D, Spiegelhalter DJ, Taylor CC, Campbell J (ed), Machine Learning, Neural and Statistical Classification, Chap. 10, Ellis HorwoodGoogle Scholar
  10. Cai X, Sowmya A, Trinder JC (2005) Learning to Recognise Roads from High Resolution Remotely Sensed Images, Proc 2nd ISSNIP:307- 312Google Scholar
  11. Cai X, Sowmya A, Trinder JC (2006) Learning parameter tuning for object extraction. In Lecture Notes in Computer Science, Narayanan PJ, Nayar SK, Shum H-Y (Ed) 3851:868- 877Google Scholar
  12. Caruana R, Niculescu-Mizil A, Crew G, Ksikes A (2004) Ensemble selection from libraries of models. Proc. 21st Int Conf Machine Learning, ACM International Conference Proceeding Series; Vol. 69, ACM Press New York:137–144Google Scholar
  13. Chanussot J, Lambert P (1998) An application of mathematical morphology to road network extraction on SAR images. Int Sym Math Morphol, Amsterdam:399–406Google Scholar
  14. Chen A, Donovan G, Sowmya A, Trinder JC (2002) Inductive Clustering: automating low-level segmentation in high resolution images. ISPRS Int Arch Photogram and Rem Sens & SIS 34 (3A):73–78.Google Scholar
  15. Chiang TY, Hsieh TH, Lau W (2001) Automatic road extraction from aerial images, Stanford Education – on-line and unpublished documentGoogle Scholar
  16. Cohen LD (1991) On active contour models and balloons. CVGIP Image Understanding 53:211–218CrossRefGoogle Scholar
  17. Dal Poz AP, Gyftakis S, Agouris P (2000) Semiautomatic road extraction: Comparison of Methodologies and experiments. ASPRS Ann Conf Washington DC USAGoogle Scholar
  18. Dell'Acqua F, Gamba P, Lisini G (2003) Road map extraction by multiple detectors in fine spatial resolution SAR data. Can J Rem Sens 29(4):481–490.Google Scholar
  19. Dial G, Gibson L, Poulsen R, (2001) Ikonos imagery and its use in automated road extraction. In Automatic extraction of Man-made Objects from Aerial and Space Images (III), Zurich (III), Baltsavias et al.(ed) 2001 Swets S Zeitlinger, Lisse: 357–366Google Scholar
  20. Doucette P, Agouris P, Musavi M, Stefanidis A (1999) Automated extraction of linear features from aerial imagery using Kohonen learning and GIS data. Lecture Notes in Computer Science, 1737:20–33CrossRefGoogle Scholar
  21. Dzeroski S, Zenko B (2002) Is combining classifiers better than selecting the best one. Proc. 19 Int. Conf. Mach. Learn:123–130Google Scholar
  22. Faber A, Förstner W (1999) Scale characteristics of local autocovariances for texture segmentation, ISPRS Int Arch Photogram Rem Sens & SIS 32(7-4-3W6): 74–78Google Scholar
  23. Ferraro M, Boccignone G, Caelli T (1999) On the representation of image structures via scale space entropy conditions. IEEE Trans. Patt Anal Mach Intell 21:1199–1203CrossRefGoogle Scholar
  24. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, MAGoogle Scholar
  25. Gruen A, Li H (1996) Linear Feature Extraction with LSB-Snakes from Multiple Images. Int Arch Photogram Rem Sens & SIS 31(B3):266–272Google Scholar
  26. Gruen A, Li H (1997) Semi-automatic linear feature extraction by dynamic programming and LSB-Snakes. Photogram Eng Rem Sens 63:985–995Google Scholar
  27. Haverkamp D, Poulsen R (2002) Complementary methods for extracting road centrelines from Ikonos imagery. Proc SPIE Image and Signal Processing for Remote Sensing VIII:501–511Google Scholar
  28. Hegarat-Mascle S, Bloch I, Vidal-Madjar D (1997) Application of Dempster-Shafer Evidence Theory to Unsupervised Classification in Multisource Remote Sensing. IEEE Trans Geosci Rem Sens 35 (4):1018–1031CrossRefGoogle Scholar
  29. Hinz S, Baumgartner A (2003) Automatic road extraction of urban road networks from multi-view aerial imagery. ISPRS J Photogram Rem Sens 58(1–2):83–98CrossRefGoogle Scholar
  30. Jeon B K, Jang J, Hong K (2000) Map based road detection in spaceborne synthetic aperture radar images based on curvilinear structure extraction. Opt Eng 39(9):2413–2421CrossRefGoogle Scholar
  31. Jin X, Davis CH (2005) An integrated system for automatic road mapping from high-resolution multi-spectral satellite imagery by information fusion, Information Fusion 6(4):257–273CrossRefGoogle Scholar
  32. Kass M, Witkin A, Terzopoulos D (1988) Snakes: Active Contour Models. Int J Computer Vision:321–331Google Scholar
  33. Katartzis A, Sahi H, Pizurica V, Cornelis J (2001) A model based approach to the automatic extraction of linear features from airborne images, IEEE Trans Geosci Rem Sens 39(9): 2073–2079CrossRefGoogle Scholar
  34. Keaton T, Brokish J (2003) Evolving roads in Ikonos Multispectral Imagery. Proc Int Conf Image Processing (ICIP)3: III-1001-4 vol 2Google Scholar
  35. Kessler O, Askin K, Beck N, Lynch J, White F, Buede D, Hall D, Llinas I, (1992) Functional description of the data fusion process, Office of Naval Technology, Naval Air Development Center, Warminster, PA, 1992.Google Scholar
  36. Kittler J, Hatef M, Duin RPW Matas J (1998) On combining classifiers. IEEE Trans PAMI 20(3):226–239Google Scholar
  37. Klein LA (1999) Sensor and Data Fusion Concepts and Applications. SPIE Optical Engineering Press, Bellingham, WAGoogle Scholar
  38. Lai JY, Sowmya A, Trinder JC, (2005) Support Vector Machine experiments for road recognition in high resolution images. Proc MLDM in Perner P and Imiya A (Ed) Machine Learning and Data Mining in Pattern Recognition, Springer Verlag, LNAI 3587, MLDM 2005 Chapter 42:426–436.Google Scholar
  39. Laptev I, Mayer H, Lindeberg T, Eckstein W, Steger C, Baumgartner A (2000) Automatic extraction of roads from aerial images based on scale space and snakes. Machine Vision Applicat 12(1):23–31CrossRefGoogle Scholar
  40. Lu Y H, Trinder JC Kubik K (2003) Automatic Building Extraction for 3D Terrain Reconstruction Using Interpretation Techniques. Proceedings ISPRS Workshop on High Resolution Mapping from Space, Hannover Germany (on CD):1–9 pagesGoogle Scholar
  41. Lu YH, Trinder JC, Kubik K (2006) Automatic Building Detection Using The Dempster-Shafer Algorithm, Photogramm Eng & Rem Sens 72(4):395–404Google Scholar
  42. Mayer H, Laptev I Baumgartner A (1998) Multi-scale and snakes for automatic road extraction. 5th Europ Conf on Comput. Vision:720–733Google Scholar
  43. Mayer H, Hinz S, Bacher U, Baltsavias E (2006) A Test of Automatic Road Extraction Approaches. Int. Archives Photogram Rem Sens & SIS 34(3) paper 0_15:1–6Google Scholar
  44. McKeown DM Denlinger JL (1988) Cooperative methods for road tracking in aerial imagery. Workshop Comput Vision Pattern Recognition:662–672Google Scholar
  45. Mena JB (2003) State of the art on automatic road extraction for GIS update: a novel classification. Pattern Recognition Letters 24(16):3037–3058CrossRefGoogle Scholar
  46. Mena JB Malpica JA (2004) An automatic method for road extraction in rural and semi-urban areas starting from high resolution satellite imagery. Pattern Recognition Letters 26: 1201–1220CrossRefGoogle Scholar
  47. Nevatia R Babu KR (1980) Linear feature extraction and description. Comp. Graph Image Process 13:257–269CrossRefGoogle Scholar
  48. Osher S, Sethian JA (1988) Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulation. J Computational Physics 79:12–49.CrossRefGoogle Scholar
  49. Quam LH (1978) Road tracking and anomaly detection in aerial imagery, Image Understanding Workshop:51–55Google Scholar
  50. Quinlan JR (1993) C4.5: Programs For Machine Learning. Morgan Kaufmann San Mateo, CAGoogle Scholar
  51. Ravanbakhsh M, Heipke C, Pakzad K (2008) Extraction of Road Junction Islands from High Resolution Aerial Imagery Using Level Sets, ISPRS Int. Archives Photogram Rem Sens and SIS 37(3A):209–214.Google Scholar
  52. Roggero M (2002) Object segmentation with region growing and principal component analysis, ISPRS Int Arch Photogram Rem Sens & SIS 34(3A):289–294Google Scholar
  53. Rottensteiner F, Trinder JC, Clode S, Kubik K (2007) Building detection by fusion of airborne laser scanner data and multi-spectral images: Performance evaluation and sensitivity analysis, ISPRS Journal Photogram Rem Sens 62:135–149CrossRefGoogle Scholar
  54. Rottensteiner F, Clode S, (2008) Building and Road Extraction by LiDAR and Imagery, in Topographic Laser Ranging and Scanning, Shan J, Toth C (Ed) CRC Press, Oxford UK.Google Scholar
  55. Sakoda W, Hu J, Pavlidis T (1993) Computer Assisted Tracking of Faint Roads in Satellite Imagery. ACSM/ASPRS Convention, New Orleans 9 (2):311–323Google Scholar
  56. Sethian JA (1985) Curvature and the evolution of fronts. Comm In Math Phys 54:487–499CrossRefGoogle Scholar
  57. Sethian JA (1995) Shape Modelling with front Propagation: A Level Set approach, IEEE Trans PAMI 17(2):158–175Google Scholar
  58. Sethian JA (1999) Level set methods and fast marching methods. Cambridge University PressGoogle Scholar
  59. Shafer G (1976) A Mathematical Theory of Evidence. Princeton University Press, Princeton, NJGoogle Scholar
  60. Shanahan J, Thomas B, Mirmehdi M, Martin T, Campbell N Baldwin J (2000), A soft computing approach to road classification. J. Intell. Robot. System 29(4):349–387CrossRefGoogle Scholar
  61. Singh S, Sowmya A (1998) RAIL: Road Recognition from Aerial Images Using Inductive Learning. ISPRS Int Arch Photogram Rem Sens SIS 32(3/1):367–378Google Scholar
  62. Strat TM (1995) Using Context to Control Computer Vision Algorithms in Automatic Extraction of Man-made Objects from Aerial and space Images, Birkhäuser Verlag Basel:3–12Google Scholar
  63. Teoh C, Sowmya A (2000) Junction Extraction from high resolution images by composite learning, ISPRS Int Arch Photogram Rem Sens & SIS 33(B3):882–888Google Scholar
  64. Tesser H, Pavlidis T (2000) Road finder front end: an automated road extraction system, IEEE Trans. Geosci. Rem Sens 38:338–341Google Scholar
  65. Trinder JC Li H (1995) Semi-Automatic Feature Extraction by Snakes, In Automatic Extraction of Man-Made Objects from Aerial and Space Images Gruen A, Kuebler O, Agouris P (Ed) Birkhäuser Verlag, Basel:95–102Google Scholar
  66. Trinder JC. Maulik U, Bandyopadhyay S (2000) Semi-Automatic Feature Extraction Using Simulated Annealing, Int Arch Photogram Rem Sens & SIS 33(3/2):905–909Google Scholar
  67. Tupin F, Maitre H (1998) Detection of linear features in SAR images: application to road network extraction. IEEE Trans. Geosci. Rem Sens 36(2):434–453CrossRefGoogle Scholar
  68. Vosselman G, Knecht D (1995) Road tracking by profile matching and Kalman filtering. In Automatic Extraction of Man-Made Objects from Aerial and Space Images Gruen A, Kuebler O, Agouris P (Ed) Birkhäuser Verlag, Basel:265–274Google Scholar
  69. Wiedemann CC, Heipke H, Mayer. Jamet O (1998) Empirical Evaluation of Automatically Extracted Road Axes. CVPR Workshop on Empirical Evaluation Methods in Computer Vision:172–187Google Scholar
  70. Wufeng C, Qiming Q (1998) A knowledge based research for road extraction from digital satellite images. Acta Scientiarum Naturalium Universitatis Pekinensis 34(2–3):54–263Google Scholar
  71. Yoon T, Park W, Kim T (2002) Normalized gradient vector diffusion and image segmentation. Proc. SPIE Remote Sensing for Environmental Monitoring, GIS Application:320–328Google Scholar
  72. Zafiropoulos P, Schenk TF (1998) Extraction of road structures with color energy models, Videmoe-TRICS VI Proc SPIE:276–290Google Scholar
  73. Zhang C, Murai S, Baltsavias E (1999) Road network detection by mathematical morphology, ISPRS Workshop on 3D Geospatial Data Production: Meeting Applicat, Requirements, Paris:85–200Google Scholar
  74. Zhao H, Kumagai J, Nakagawa M, Shibasaki R (2002) Semi automatic road extraction from high resolution satellite image, ISPRS Int Arch Photogram Rem Sens & SIS 34(3A):406–411Google Scholar
  75. Ziems M, Gerke M, Heipke C (2007) Automatic Road Extraction from Remotely Sensed Imagery Incorporating Prior Information and Colour Segmentation. Int. Arch Photogram Rem Sens & SIS 36(3/W49A):141–147Google Scholar
  76. Zlotnick A, Carnine PD (1993) Finding roads seeds in aerial images. CVGIP Image Understanding 57(2):243–260CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • John Trinder
    • 1
  1. 1.School of Surveying and Spatial Information SystemsThe University of New South WalesSydneyAustralia

Personalised recommendations