NDLI: Satellite image segmentation using Self- Organizing Maps and Fuzzy C-Means

Content Provider	IEEE Xplore Digital Library
Author	Awad, M.M. Nasri, A.
Copyright Year	2009
Description	Author affiliation: American University of Beirut - Computer Sc. Department, Lebanon (Nasri, A.) \|\| National Council for Scientific Research, P. O. Box 11-8281, Beirut, Lebanon, 11072260 (Awad, M.M.)
Abstract	The quality of image interpretation depends strongly on the segmentation process which is an important step in image processing. Most of the segmentation methods and approaches are not suitable for noisy environments such as satellite images of high resolution. Sometime they require a priori knowledge, and another time they do not work on all types of images. Self-Organizing Maps (SOMs) and Fuzzy C-Means (FCM) segmentation methods are widely used to process different types of simple and complex images. These two important known methods are reviewed, and summarized. In addition, a new approach is created based on SOMs and FCM. The reason for combining both methods is to create an unsupervised parameter free approach. The new approach is applied on two different types of medium and high resolution satellite images in order to examine the accuracy of the segmentation methods and the new approach. This paper and the results of experiments provide the reader with information about the improvement obtained by this approach compared to known commercial segmentation method.
Starting Page	398
Ending Page	402
File Size	1379985
Page Count	5
File Format	PDF
ISBN	9781424459490
e-ISBN	9781424459506
DOI	10.1109/ISSPIT.2009.5407521
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-14
Publisher Place	United Arab Emirates
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Satellites Image segmentation Self organizing feature maps Neurons Neural networks Fuzzy logic Image resolution Genetic algorithms Testing Clustering algorithms Satellite image Segmentation Remote sensing Self-Organizing Maps Fuzzy C-Means Unsupervised
Content Type	Text
Resource Type	Article

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