12 May 2010 Improved outlier identification in hyperspectral imaging via nonlinear dimensionality reduction
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Abstract
We use a nonlinear dimensionality reduction technique to improve anomaly detection in a hyperspectral imaging application. A nonlinear transformation, diffusion map, is used to map pixels from the high-dimensional spectral space to a (possibly) lower-dimensional manifold. The transformation is designed to retain a measure of distance between the selected pixels. This lower-dimensional manifold represents the background of the scene with high probability and selecting a subset of points reduces the computational overhead associated with diffusion map. The remaining pixels are mapped to the manifold by means of a Nystr¨om extension. A distance measure is computed for each new pixel and those that do not reside near the background manifold, as determined by a threshold, are identified as anomalous. We compare our results with the RX and subspace RX methods of anomaly detection.
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C. C. Olson, J. M. Nichols, J. V. Michalowicz, F. Bucholtz, "Improved outlier identification in hyperspectral imaging via nonlinear dimensionality reduction", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769507 (12 May 2010); doi: 10.1117/12.851811; https://doi.org/10.1117/12.851811
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