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Improvement of building extraction using decision fusion of locally and globally enhanced IKONOS images
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
2008 (English)In: Proceedings of SPIE - The International Society for Optical Engineering, 2008Conference paper (Refereed)
Abstract [en]

In this paper a fully automated algorithm for building extraction from remote sensing IKONOS images is presented. Local and global enhancement of an original image improves the rate of building detection in some cases. However, some undesirable effects could occur due to image enhancement. As a result the Bayesian classification method which has been previously used could result in errors. To deal with such problems, decision fusion is used together with a shadow-based verification step to achieve a better result from locally and globally enhanced classified images. Experimental results justify the efficiency of the proposed method in dealing with the problem of building extraction in IKONOS images.

Place, publisher, year, edition, pages
, Proceedings of SPIE - The International Society for Optical Engineering, ISSN 0277-786X ; 7266
Keyword [en]
Bayesian discriminator, Histogram equalization, Image laplacian, Unsharp mask filter, Automated algorithms, Bayesian classifications, Building detections, Building extractions, Decision fusions, Global enhancements, Ikonos images, Original images, Undesirable effects, Bayesian networks, Buildings, Discriminators, Laplace transforms, Mining equipment, Remote sensing, Image enhancement
National Category
Computer Science
URN: urn:nbn:se:kth:diva-154161DOI: 10.1117/12.807592ScopusID: 2-s2.0-62449160292OAI: diva2:756216
Optomechatronic Technologies 2008, 17 November 2008 through 19 November 2008, San Diego, CA

QC 20141016

Available from: 2014-10-16 Created: 2014-10-14 Last updated: 2014-10-16Bibliographically approved

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Bahadorian, Mitra
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Numerical Analysis and Computer Science, NADA
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ReferencesLink to record
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