Clustering is central to many image processing and remote sensing applications. ISODATA is one of the most popular and widely used clustering methods in geoscience applications, but it can run slowly, particularly with large data sets. We present a more efficient approach to ISODATA clustering, which achieves better running times by storing the points in a kd-tree and through a modification of the way in which the algorithm estimates the dispersion of each cluster. We also present an approximate version of the algorithm which allows the user to further improve the running time, at the expense of lower fidelity in computing the nearest cluster center to each point. We provide both theoretical and empirical justification that our modified approach produces clusterings that are very similar to those produced by the standard ISODATA approach. We also provide empirical studies on both synthetic data and remotely sensed Landsat and MODIS images that show that our approach has significantly lower running times.
|Number of pages||33|
|Journal||International Journal of Computational Geometry and Applications|
|State||Published - Feb 2007|
Bibliographical noteFunding Information:
Clustering is central to many image processing and remote sensing applications, ISODATA is one of the most popular and widely used clustering methods in geoseienee applications, *A preliminary version of this paper appeared in Proceedings of the IEEE International Geoseienee and Remote Sensing Symposium (IGARSS'03), Toulouse, France, 2003, Vol. Ill, 2057-2059. tThe work of this author was supported by the National Science Foundation under grant CCR-0098151. tThe author was previously with the Applied Information Sciences Branch, NASA Goddard Space Flight Center.
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