Anomaly clustering in hyperspectral images

Timothy J. Doster; David S. Ross; David W. Messinger; William F. Basener

doi:10.1117/12.818407

27 April 2009 Anomaly clustering in hyperspectral images

Timothy J. Doster, David S. Ross, David W. Messinger, William F. Basener

Proceedings Volume 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV; 73341P (2009) https://doi.org/10.1117/12.818407
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Timothy J. Doster, David S. Ross, David W. Messinger, and 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); https://doi.org/10.1117/12.818407

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