Resampling approach for anomaly detection in multispectral images

James P. Theiler; D. Michael Cai

doi:10.1117/12.487069

23 September 2003 Resampling approach for anomaly detection in multispectral images

James P. Theiler, D. Michael Cai

Proceedings Volume 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX; (2003) https://doi.org/10.1117/12.487069
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

We propose a novel approach for identifying the "most unusual" samples in a data set, based on a resampling of data attributes. The resampling produces a "background class" and then binary classification is used to distinguish the original training set from the background. Those in the training set that are most like the background (i.e., most unlike the rest of the training set) are considered anomalous. Although by their nature, anomalies do not permit a positive definition (if I knew what they were, I wouldn't call them anomalies), one can make "negative definitions" (I can say what does not qualify as an interesting anomaly). By choosing different resampling schemes, one can identify different kinds of anomalies. For multispectral images, anomalous pixels correspond to locations on the ground with unusual spectral signatures or, depending on how feature sets are constructed, unusual spatial textures.

Citation Download Citation

James P. Theiler and D. Michael Cai "Resampling approach for anomaly detection in multispectral images", Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); https://doi.org/10.1117/12.487069

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