NDLI: Classification of remote sensing images from urban areas using a fuzzy possibilistic model

Content Provider	IEEE Xplore Digital Library
Author	Chanussot, J. Benediktsson, J.A. Fauvel, M.
Copyright Year	2004
Abstract	The classification of very high-resolution remotely sensed images from urban areas is addressed. Previous studies have shown the interest of exploiting the local geometrical information of each pixel to improve the classification. This is performed using the derivative morphological profile (DMP) obtained with a granulometric approach, using opening and closing operators. For each pixel, this DMP constitutes the feature vector on which the classification is based. In this letter, we present an interpretation of the DMP in terms of a fuzzy measurement of the characteristic size and contrast of each structure. This fuzzy measure can be compared to predefined possibility distributions to derive a membership degree for a set of given classes. The decision is taken by selecting the class with the highest membership degree. This model is illustrated and validated in a classification problem using IKONOS images.
Sponsorship	IEEE Geoscience and Remote Sensing Society
Starting Page	40
Ending Page	44
Page Count	5
File Size	283748
File Format	PDF
ISSN	1545598X
Volume Number	3
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Remote sensing Urban areas Fuzzy sets Image segmentation Laboratories Feature extraction Image texture analysis Size measurement Morphology Pixel very high resolution Classification fuzzy sets granulometry mathematical morphology possibility distribution urban area
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Geotechnical Engineering and Engineering Geology

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