Abstract
In this paper the approach for automatic road extraction for an urban region using structural, spectral and geometric characteristics of roads has been presented. Roads have been extracted based on two levels: Pre-processing and road extraction methods. Initially, the image is pre-processed to improve the tolerance by reducing the clutter (that mostly represents the buildings, parking lots, vegetation regions and other open spaces). The road segments are then extracted using Texture Progressive Analysis (TPA) and Normalized cut algorithm. The TPA technique uses binary segmentation based on three levels of texture statistical evaluation to extract road segments where as, Normalized cut method for road extraction is a graph based method that generates optimal partition of road segments. The performance evaluation (quality measures) for road extraction using TPA and normalized cut method is compared. Thus the experimental result show that normalized cut method is efficient in extracting road segments in urban region from high resolution satellite image.
Similar content being viewed by others
References
Baumgartner A, Steger C, Mayer H, Eckstein W and Ebner H (1999) Automatic road extraction based on multiscale, grouping and context. Photogrammetric Engineering Remote Sensing 65:777–785
Bhagavathy S and Manjunath BS (2006) Modeling and Detection of Geospatial Objects Using Texture Motifs. IEEE Transactions on Geoscience Remote Sensing 44(12): 3706–3715
Chen CH, Pau L F and Wang P SP (1998) The Handbook of Pattern Recognition and Computer Vision (2nd Edition), by World Scientific Publishing Co
Comaniciu D and Meer P (2002) Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5): 603–619
Doucette P, Agouris P, Stefanidis A and Musavi M. (2001) Self-organized clustering for road extraction in classified imagery. ISPRS Journal of Photogrammetry Remote Sensing 55(5):347–358
Grote A, Butenuth M, Gerke M and Heipke C (2007) Segmentation Based on Normalized Cuts for the Detection of Suburban Roads in Aerial Imagery. IEEE Proc. Urban Remote Sensing Joint Event 1–5
Gruen A, Agouris P and Li H (1995) Linear feature extraction with dynamic programming and globally enforced least squares matching. Automatic Extraction of Man-Made Objects from Aerial and Space images. pp 83–94
Gruen A and Li H (1997) Semi-automatic linear feature extraction by dynamic programming and LSB-snakes. Photogrammetric Engineering Remote Sensing 63(8):985–995
Guindon B (2000) A framework for the development and assessment of object recognition modules from high-resolution satellite images. Canadian Journal of Remote Sensing 26(4):334–348
Haralick RM, Shanmugam K and Dinstein I (1973) Textural features for image classification. IEEE Transactions on Systems. Man. and Cybernetics 3(6):610–621
Haverkamp D and Poulsen R (2002) Complementary methods for extracting road centrelines from IKONOS imagery. In Proceedings of Image and Signal Processing for Remote Sensing VIII SPIE. Vol. 4885. pp 501–511
Hinz S, Baumgartner A, Steger C, Mayer H, Eckstein W, Ebner H and Radig B (1999) Road Extraction in Rural Areas and Urban Areas, Semantic Modeling for the Aquisition of Topographic Information from Images and Maps. pp 133–153
Jin X and Davis CH (2003) Automatic road extraction from high-resolution multi-spectral IKONOS imagery. In Proceedings of International Geosciences and Remote Sensing Symposium. pp 1730–1732
Kass M, Witkin A and Terzopoulos D (1988). Snakes: active contour models. International Journal on Computer Vision. 1(4):321–331
McKeown D and Denlinger J (1988) Cooperative methods for road tracking in aerial imagery. IEEE Proc. Computer Vision and Pattern Recognition 662–672
Mena JB and Malpica JA (2005). An automatic method for road extraction in rural and semi-urban areas starting from high resolution satellite imagery. Pattern Recognition Letters 26(9):1201–1220
Mumford D and Shah J (1989) Optimal approximations by piecewise smooth functions, and associated variational problems. Comm. Pure Math. XLII:577–684
Neuenschwander W, Fua P, Szekely G and Kubler O (1995) From ziplock snakes to velcro surfaces. In Automatic Extraction of Man-Made Objects from Aerial and Space Images. pp 105–114
Omkar SN, Manoj KM, Mudigere D and Muley D (2007) Urban Satellite Image Classification using Biologically Inspired Techniques. IEEE International Symposium on Industrial Electronics 1767–1772
Peteri R and Ranchin T (2004) Multiresolution snakes for urban road extraction from Ikonos and QuickBird images, 23rd EARSeL Annual Symposium — Remote Sensing in Transition. Ghent. Belgium
Shackelford AK and Davis CH (2003) A hierarchical fuzzy classiûcation approach for high-resolution multispectral data over urban areas. IEEE Transactions on Geoscience and Remote Sensing 41(9):1920–1932
Stathakis D and Vasilakos A (2006) Comparison of Computational Intelligence Based Classification Techniques for Remotely Sensed Optical Image Classification. IEEE Transactions on Geoscience and Remote Sensing 44(8):2305–2318
Shi J, Belongie S, Leung T and Malik J (1998) Image and Video Segmentation: The Normalized Cut Framework. Image Processing: ICIP 98 Proceedings. vol. 1:943–947
Shi J and Malik J (2000) Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8): 888–905
Shi W and Zhu C (2002) The line segment match method for extracting road network from high-resolution satellite images. IEEE Transactions on Geoscience and Remote Sensing 40(2):511–514
Trinder J and Li H (1995) Semi-automatic feature extraction by snakes, Automatic Extraction of Man-Made Objects from Aerial and Space Images. pp 95–104
Vosselman G and Knecht J (1995) Road tracing by profile matching and Kalman filtering, Automatic Extraction of Man-Made Objects from Aerial and Space Images. pp 265–274
Wiedemann, C., Heipke, C., Mayer, H. and Hinz, S. (1998). Automatic extraction and evaluation of road networks from MOMS-2P imagery. International Archives of Photogrammetry and Remote Sensing 32(3): 485–489
Wiedemann C, Heipke C, Mayer H and Jamet O (1998) Empirical evaluation of automatically extracted road axes. Empirical Evaluation Methods in Computer Vision. IEEE Computer Society Press 172–187
Wu Z and Leahy R (1993). An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 15(11): 1101–1113
Xiaoying J and Davis CH (2005) An integrated system for automatic road mapping from high-resolution multi-spectral satellite imagery by information fusion. Information Fusion. pp 257–273
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Senthilnath, J., Rajeshwari, M. & Omkar, S.N. Automatic road extraction using high resolution satellite image based on texture progressive analysis and normalized cut method. J Indian Soc Remote Sens 37, 351–361 (2009). https://doi.org/10.1007/s12524-009-0043-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-009-0043-5