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Object-Oriented Image Processing Of An High Resolution Satellite Imagery With Perspectives For Urban Growth, Planning And Development
Afroz Shaik Mohammed1, Shaik Rusthum2
Pages - 18 - 28     |    Revised - 06-08-2008     |    Published - 16-09-2008
Volume - 2   Issue - 3    |    Publication Date - June 2008  Table of Contents
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KEYWORDS
Object Oriented, Classification, Segmentation, Spatial Information, Accuracy Assessment, Urban Morphology.
ABSTRACT
The management of urban areas by urban planners relies on detailed and updated knowledge of their nature and distribution. Manual photo-interpretation of aerial photographs is efficient, but is time consuming. Image segmentation and object-oriented classifications provide a tool to automatically delineate and label urban areas. Here single pixels are not classified but objects created in multi-resolution segmentation process, which allows use of, spectral responses but also texture, context and information from other object layers. This paper presents a methodology allowing to derive meaningful area-wide spatial information for city development and management from high resolution imagery. Finally, the urban land cover classification is used to compute a spatial distribution of built-up densities within the city and to map homogeneous zones or structures of urban morphology
CITED BY (3)  
1 Usman, B. (2013). Satellite Imagery Land Cover Classification using K-Means Clustering Algorithm Computer Vision for Environmental Information Extraction.
2 Babawuro, U., & Beiji, Z. (2012). Automated Extraction of Geospatial Features from Satellite Imagery: Computer Vision Oriented Plane Surveying. International Journal of Computer Science Issues(IJCSI), 9(6).
3 Vira, N., & Vira, S. (2009). Detection of a Virtual Passive Pointer. International Journal of Image Processing (IJIP), 3(2), 55.
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1 “Erdas Imagine, 8.5”, User Guide, 2001
2 T.M. Lillesand, and R.W. Kiefer. “Remote Sensing and Image Interpretation”. 4th ed, John Wiley and Sons, inc. USA, 2001, ISBN: 0471255157, 2001
3 Mather, M.Paul. “Computer Processing of Remotely-Sensed Images”. St Edmundsbury Press Ltd., Bury St Edmunds, Suffolk, Wiley and Sons, ISBN: 0- 471-90648-4,1987
4 “ECognition user guide”, Concept and Methods, 2001
5 U.C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, M. Heynen. “Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS - ready information”. ISPRS Journal of Photogrammetry and Remote Sensing, 58: 239- 258, 2004
6 N. Goward, F. Huemmrich, and H. Waring. “Visible-near infrared Spectral reflectance of landscape components in western Oregon”. Remote Sensing environment, 47:190-203, 1994
7 D. Klimešová, E. Ocelíková. “Spatial data in land management and local government”. Proceedings of the 4th Conference of the European Federation for Information Technology in Agriculture. Debreceen, 363-368, 2003
8 S.P.S. Kushwaha, S.K. Subramanian, G.CH. Chennaiah, J. Ramana Murthy, S.V.C. Kameswara Rao, A. Perumal, and G. behera. “International Remote Sensing and GIS methods for sustainable rural development”. International Journal of Remote Sensing, 17: 3055-3069, 1996
9 J.C. Price. “How unique are spectral signatures?”. Remote Sensing of Environment, 49: 181- 186, 1994
Mr. Afroz Shaik Mohammed1
Deccan college of Engg. and Technology, - India
smafroz@yahoo.com
Professor Shaik Rusthum2
VIF College of Engg. & Technology - India