Feature-based anomaly detection

Mark J. Carlotto

doi:10.1117/12.721149

16 May 2007 Feature-based anomaly detection

Mark J. Carlotto

Proceedings Volume 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI; 65671A (2007) https://doi.org/10.1117/12.721149
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

A feature-based approach for detecting anomalies in spectral, spatial, temporal, and other domains is described. When the frequency of occurrence is small relative to the background, anomalies such as man-made objects in natural image backgrounds do not form their own clusters, but are instead assigned the nearest background cluster, becoming an outlier (statistical anomaly) in that cluster. Our method clusters data, which may be spectral, spatial, or temporal in nature, into one or more background types and computes the Mahalanobis distance between the data and assigned model (background cluster). The detection of a variety of objects and phenomena in panchromatic and multispectral imagery, and video are illustrated.

Citation Download Citation

Mark J. Carlotto "Feature-based anomaly detection", Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 65671A (16 May 2007); https://doi.org/10.1117/12.721149

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