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Proceedings Paper

Segmentation of remote sensing images using multistage unsupervised learning
Author(s): Murat Sezgin; Okan K. Ersoy; Bingül Yazgan
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Paper Abstract

In this study, we investigate an unsupervised learning algorithm for the segmentation of remote sensing images in which the optimum number of clusters is automatically estimated, and the clustering quality is checked. The computational load is also reduced as compared to a single stage algorithm. The algorithm has two stages. At the first stage of the algorithm, the self-organizing map was used to obtain a large number of prototype clusters. At the second stage, these prototype clusters were further clustered with the K-means clustering algorithm to obtain the final clusters. A clustering validity checking method, Davies-Bouldin validity checking index, was used in the second stage of the algorithm to estimate the optimal number of clusters in the data set.

Paper Details

Date Published: 2 November 2004
PDF: 8 pages
Proc. SPIE 5558, Applications of Digital Image Processing XXVII, (2 November 2004); doi: 10.1117/12.558963
Show Author Affiliations
Murat Sezgin, Istanbul Technical Univ. (Turkey)
Okan K. Ersoy, Purdue Univ. (United States)
Bingül Yazgan, Istanbul Technical Univ. (Turkey)

Published in SPIE Proceedings Vol. 5558:
Applications of Digital Image Processing XXVII
Andrew G. Tescher, Editor(s)

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