27 April 2009 Anomaly clustering in hyperspectral images
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Abstract
The topological anomaly detection algorithm (TAD) differs from other anomaly detection algorithms in that it uses a topological/graph-theoretic model for the image background instead of modeling the image with a Gaussian normal distribution. In the construction of the model, TAD produces a hard threshold separating anomalous pixels from background in the image. We build on this feature of TAD by extending the algorithm so that it gives a measure of the number of anomalous objects, rather than the number of anomalous pixels, in a hyperspectral image. This is done by identifying, and integrating, clusters of anomalous pixels via a graph theoretical method combining spatial and spectral information. The method is applied to a cluttered HyMap image and combines small groups of pixels containing like materials, such as those corresponding to rooftops and cars, into individual clusters. This improves visualization and interpretation of objects.
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Timothy J. Doster, Timothy J. Doster, David S. Ross, David S. Ross, David W. Messinger, David W. Messinger, William F. Basener, William F. Basener, } "Anomaly clustering in hyperspectral images", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341P (27 April 2009); doi: 10.1117/12.818407; https://doi.org/10.1117/12.818407
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