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29 December 2000 Unsupervised segmentation algorithm based on an iterative spectral dissimilarity measure for hyperspectral imagery
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Abstract
We present an adaptive unsupervised segmentation technique, in which spectral features are obtained and processed without a priori knowledge of the spectral characteristics. The proposed technique is based on an iterative method, in which segmentation at a given iteration depends closely on the segmentation results at the previous iteration. The hyperspectral images are first coarsely segmented and then the segmentation is successively refined via an iterative spectral dissimilarity measure. The algorithm also provides reduced computational complexity and improved segmentation performance. The algorithm consists of (1) an initial segmentation based on a fixed spectral dissimilarity measure and the k-means algorithm, and (2) subsequent adaptive segmentation based on an iterative spectral dissimilarity measure over a local region whose size is reduced progressively. The iterative use of a local spectral dissimilarity measure provided a set of values that can discriminate among different materials. The proposed unsupervised segmentation technique proved to be superior to other unsupervised algorithms, especially when a large number of different materials are mixed in complex hyperspectral scenes.
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Heesung Kwon, Sandor Z. Der, and Nasser M. Nasrabadi "Unsupervised segmentation algorithm based on an iterative spectral dissimilarity measure for hyperspectral imagery", Proc. SPIE 4310, Visual Communications and Image Processing 2001, (29 December 2000); https://doi.org/10.1117/12.411792
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