Segmentation of remote sensing images using multistage unsupervised learning

Murat Sezgin; Okan K. Ersoy; Bingül Yazgan

doi:10.1117/12.558963

2 November 2004 Segmentation of remote sensing images using multistage unsupervised learning

Murat Sezgin, Okan K. Ersoy, Bingül Yazgan

Proceedings Volume 5558, Applications of Digital Image Processing XXVII; (2004) https://doi.org/10.1117/12.558963
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

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.

Citation Download Citation

Murat Sezgin, Okan K. Ersoy, and Bingül Yazgan "Segmentation of remote sensing images using multistage unsupervised learning", Proc. SPIE 5558, Applications of Digital Image Processing XXVII, (2 November 2004); https://doi.org/10.1117/12.558963

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