Hyperspectral image processing using locally linear embedding

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.