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Unsupervised Building Extraction from High Resolution Satellite Images Irrespective of Rooftop Structures
Lizy Abraham, M.Sasikumar
Pages - 219 - 232     |    Revised - 15-07-2012     |    Published - 10-08-2012
Volume - 6   Issue - 4    |    Publication Date - August 2012  Table of Contents
Pan Sharpened Multispectral Image, Rooftop Detection, Otsu’s Thresholding, Area Analysis
Extraction of geospatial data from the photogrammetric sensing images becomes more and more important with the advances in the technology. Today Geographic Information Systems are used in a large variety of applications in engineering, city planning and social sciences. Geospatial data like roads, buildings and rivers are the most critical feeds of a GIS database. However, extracting buildings is one of the most complex and challenging tasks as there exist a lot of inhomogeneity due to varying hierarchy. The variety of the type of buildings and also the shapes of rooftops are very inconstant. Also in some areas, the buildings are placed irregularly or too close to each other. For these reasons, even by using high resolution IKONOS and QuickBird satellite imagery the quality percentage of building extraction is very less. This paper proposes a solution to the problem of automatic and unsupervised extraction of building features irrespective of rooftop structures in multispectral satellite images. The algorithm instead of detecting the region of interest, eliminates areas other than the region of interest which extract the rooftops completely irrespective of their shapes. Extensive tests indicate that the methodology performs well to extract buildings in complex environments.
CITED BY (5)  
1 Zakharov, A., Tuzhilkin, A., & Zhiznyakov, A. (2015, December). Automatic building detection from satellite images using spectral graph theory. In 2015 International Conference on Mechanical Engineering, Automation and Control Systems (MEACS) (pp. 1-5). IEEE.
2 Osaki, K. (2015). Appropriate Luminance for Estimating Vegetation Index from Digital Camera Images. Bull. Soc. Photogr. Imag. Japan, 25(2), 31-37.
3 Abraham, L., & Sasikumar, M. (2014). Analysis of satellite images for the extraction of structural features. IETE Technical Review, 31(2), 118-127.
4 Sasikumar, M., & Moni, R. S. (2014). Use of Discrete Sine Transform for A Novel Image Denoising Technique.
5 Abraham, L., & Sasikumar, M. Structural Feature Extraction from Satellite Images.
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1 Henricsson O., “The Role of Color Attributes and Similarity Grouping in 3-D Building Reconstruction”, Computer Vision and Image Understanding, 1998.
2 A. Fischer, T.H.Kolbe, F. Lang, A. B. Cremers, W. F¨orstner, L. Pl¨umer, and V.Steinhage, “Extracting buildings from aerial images using hierarchical aggregation in 2D and 3D”, Computer Vision and Image Understanding, pages 185–203, 1998.
3 T. Moons, D. Fr`ere, J. Vandekerckhove, and L. Van Gool, “Automatic modeling and 3D reconstruction of urban house roofs from high resolution aerial imagery”, In Proceedings,Fifth European Conference on Computer Vision, Vol. I, pages. 410–425, 1998.
4 D. Koc San and M. Turker, “Automatic Building Extraction From High Resolution Stereo Satellite Images”, ISPRS commission VII, 2007
5 G. Sohn and J. Dowman, "Building Extraction Using LIDAR DEMs and IKONOS Images",ISPRS, Volume XXXIV, PART 3/W13, 2003.
6 F. Rottensteiner and C. Briese., “A New Method for Building Extraction in Urban Areas from High-Resolution LIDAR Data”, Symposium der ISPRS-Comm. III. International Archives of Photogrammetry and Remote Sensing, Volume XXXIV / 3A, pp. 295 – 301,2002.
7 Yanfeng Wei, Zhongming Zhao, and Jianghong Song. “Urban building extraction from high-resolution satellite panchromatic image using clustering and edge detection”,Geoscience and Remote Sensing Symposium, 2005.
8 S.D Mayungaa , Dr. Y. Zhanga, and Dr. D.J. Colemana, “Semi-Automatic Building Extraction Utilizing Quickbird Imagery”, IAPRS Vol. XXXVI, Part 3/W24, 2005.
9 Jin, X. and Davis, C. H., “Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information,” EURASIP Journal on Applied Signal Processing, 2196–2206 (2005).
10 H. Akçay and S. Aksoy, “Automatic detection of geospatial objects using multiple hierarchical segmentations,” IEEE Trans. Geosci. Remote Sens.,vol. 46, no. 7, pp. 2097–2111, Jul. 2008.
11 Ö. Aytekin, A. Erener, I. Ulusoy, H. S. Düzgün, “Automatic and Unsupervised Building Extraction in Complex Urban Environments from Multi-Spectral Satellite Imagery”, 4th International Conference on Recent Advances in Space Technologies, Space for the Developing World, RAST 2009, Istanbul, Turkey, June 2009.
12 Katartzis, A. and Sahli, H., “A stochastic framework for the identification of building rooftops using a single remote sensing image,” IEEE Transactions on Geoscience and Remote Sensing 46, 259 – 271 (2007).
13 B. Sirmacek and C. Unsalan, "A probabilistic framework to detect buildings in aerial and satellite images", IEEE Transactions on Geoscience and Remote Sensing, Vol. 49 (1),pp. 211-221, January 2011.
14 Hazelhoff, L., De With, P.: “Localizations of buildings with a gable roof in veryhighresolution aerial images” In: Proceedings of IS&T SIE Electronic Imaging, Visual Information Processing and Communication II (2011).
15 Tsai, 2006, “A comparative study on shadow compensation of color aerial images in invariant color models”, IEEE Transactions On Geoscience And Remote Sensing, vol. 44,no. 6, June 2006.
16 N. Otsu, "A threshold selection method from graylevel histograms," IEEE Trans. Sys.Man Cyber., vol. 9, no. 1, pp. 62-66, 1979.
17 D. Comaniciu and P. Meer. Mean shift: "A robust approach toward feature space analysis”, IEEE Trans. Pattern Anal. Machine Intell., 24:603–619, 2002.
18 L. da F. Costa and R. M. Cesar Jr., “Shape Analysis and Classification: Theory and Practice”, CRC Press, Boca Raton, Fla, USA, 2001.
19 J. A. Shufelt, “Performance evaluation and analysis of monocular building extraction from aerial imagery,” IEEE Trans. Pattern Anal. Machine Intell., vol. 21, no. 4, pp. 311–326,1999.
20 D. S. Lee, J. Shan, and J. S. Bethel, “Class-guided building extraction from Ikonos imagery,” Photogrammetric Engineering and Remote Sensing, vol. 69, no. 2, pp. 143–150, 2003.
Mr. Lizy Abraham
Dr. M.Sasikumar
Marian Engineering College - India