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Proceedings Paper

Feature-based anomaly detection
Author(s): Mark J. Carlotto
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Paper 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.

Paper Details

Date Published: 16 May 2007
PDF: 7 pages
Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 65671A (16 May 2007); doi: 10.1117/12.721149
Show Author Affiliations
Mark J. Carlotto, General Dynamics Advanced Information Systems (United States)

Published in SPIE Proceedings Vol. 6567:
Signal Processing, Sensor Fusion, and Target Recognition XVI
Ivan Kadar, Editor(s)

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