Motion based unsharp masking [MUSM] for extracting building from urban images
2008 (English)Conference paper (Refereed)
In recent decades, classification of remote sensing images from urban area as a means to achieve necessitated information for some applications such as automatic map updating and GIS, planning and emergency response has become one of the challenging subjects for image processing researches. In this paper, a method for classification of remote sensing image from urban area is addressed. First, motion based unsharp masking [MUSM] is applied to the input image to enhance its high frequency components. Then, laplacian of image as input feature for the Bayesian classifier is utilized. After that, size filter is used for large and small building discrimination. The Classification of small and large building using unsharp mask and Bayesian discrimination function has increased in aspect of accuracy in comparison with original Bayesian method for Classification of urban area. Experiments justify the efficiency of the proposed approach.
Place, publisher, year, edition, pages
2008. 1280-1284 p.
, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, ISSN 1062-922X
Building extraction, Classification, IKONOS images, Motion based unsharp masking, Remote sensing image, Bayesian networks, Control theory, Cybernetics, Image reconstruction, Regional planning, Remote sensing, Buildings
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-154116DOI: 10.1109/ICSMC.2008.4811460ScopusID: 2-s2.0-69949141643OAI: oai:DiVA.org:kth-154116DiVA: diva2:758089
2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008, 12 October 2008 through 15 October 2008, Singapore
QC 201410242014-10-242014-10-142014-10-24Bibliographically approved