Departmental Papers (BE)

Document Type

Conference Paper

Date of this Version

March 2003


We describe a method of processing hyperspectral images of natural scenes that uses a combination of k-means clustering and locally linear embedding (LLE). The primary goal is to assist anomaly detection by preserving spectral uniqueness among the pixels. In order to reduce redundancy among the pixels, adjacent pixels which are spectrally similar are grouped using the k-means clustering algorithm. Representative pixels from each cluster are chosen and passed to the LLE algorithm, where the high dimensional spectral vectors are encoded by a low dimensional mapping. Finally, monochromatic and tri-chromatic images are constructed from the k-means cluster assignments and LLE vector mappings. The method generates images where differences in the original spectra are reflected in differences in the output vector assignments. An additional benefit of mapping to a lower dimensional space is reduced data size. When spectral irregularities are added to a patch of the hyperspectral images, again the method successfully generated color assignments that detected the changes in the spectra.


Copyright 2003 IEEE. Reprinted from Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering 2003, pages 316-319.
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hyperspectral image, locally linear embedding, anomaly detection



Date Posted: 09 November 2004

This document has been peer reviewed.