Commenced in January 2007
Paper Count: 32009
Automatic Extraction of Arbitrarily Shaped Buildings from VHR Satellite Imagery
Abstract:Satellite imagery is one of the emerging technologies which are extensively utilized in various applications such as detection/extraction of man-made structures, monitoring of sensitive areas, creating graphic maps etc. The main approach here is the automated detection of buildings from very high resolution (VHR) optical satellite images. Initially, the shadow, the building and the non-building regions (roads, vegetation etc.) are investigated wherein building extraction is mainly focused. Once all the landscape is collected a trimming process is done so as to eliminate the landscapes that may occur due to non-building objects. Finally the label method is used to extract the building regions. The label method may be altered for efficient building extraction. The images used for the analysis are the ones which are extracted from the sensors having resolution less than 1 meter (VHR). This method provides an efficient way to produce good results. The additional overhead of mid processing is eliminated without compromising the quality of the output to ease the processing steps required and time consumed.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474767Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 598
 Ali Ozgun Ok, Caglar Senaras, and Baris Yuksel, “Automated Detection of Arbitrarily Shaped Buildings in Complex Environments from Monocular VHR Optical Satellite Imagery” IEEE transactions on Geoscience and Remote Sensing, vol. 51, no. 3, March 2013.
 Fischer, T. H. Kolbe, F. Lang, A. B. Cremers, W. Förstner, L. Plümer, and V. Steinhage, “Extracting buildings from aerial images using hierarchical aggregation in 2D and 3D,” Computer Vision Image Understand., Nov. 1998.
 H. G. Akçay and S. Aksoy, “Building detection using directional spatial constraints,” in Proc. IEEE IGARSS, 2010.
 H. Mayer, “Automatic object extraction from aerial imagery -- A survey focusing on buildings,” Computer Vision Image Understand., May 1999.
 Imdad Ali Rizvi and B.K.Mohan, “Object-Based Image Analysis of High-Resolution Satellite Images Using Modified Cloud Basis Function Neural Network and Probabilistic Relaxation Labeling Process” IEEE Trans. on Geoscience and Remote Sensing, vol. 49(12), Jan. 2011.
 E. Baltsavias, “Object extraction and revision by image analysis using existing Geodata and knowledge: Current status and steps towards operational systems,” ISPRS J. Photogramm. Remote Sens., vol. 58, no. 3/4, pp. 129–151, Jan. 2004.
 D. Koc San, “Approaches for Automatic Urban Building Extraction and Updating From High Resolution Satellite Imagery,” Ph.D. Thesis, Middle East Tech. Univ., Ankara, Turkey, 2009.
 R. B. Irvin and D. M. McKeown Jr., “Methods for exploiting the relationship between buildings and their shadows in aerial imagery,” IEEE Trans. Syst., Man, Cybern., vol. 19, no. 6, pp. 1564–1575, Nov./Dec. 1989.
 A. Huertas and R. Nevatia, “Detecting buildings in aerial images,” Computer Vision, Graph. Image Process, vol. 41, no. 2, pp. 131–152, Feb. 1988.
 J. G. Liu, “Smoothing filter-based intensity modulation: A spectral preserve image fusion Technique for improving spatial details,” Int. J. Remote Sens., Jan. 2000.
 M. Teke, E. Ba¸seski, A. Ö. Ok, B. Yüksel, and Ç.¸Senaras, “Multispectral false color shadow detection,” in Photogrammetric Image Analysis, vol. 6952, U. Stilla, F. Rottensteiner, H. Mayer, B. Jutzi, and M.Butenuth, Eds. Berlin,Germany: Springer- Verlag, 2011, pp. 109–119.
 I. Bloch, “Fuzzy relative position between objects in image processing: A morphological approach,” IEEE Trans. Pattern Anal. Mach. Intell., Jul. 1999.
 C. Rother, V. Kolmogorov, and A. Blake, “GrabCut: Interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph. (TOG), 2004.