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Anomaly detection in hyperspectral imagery using stable distribution
Conference
Mercan, S, Alam, MS. (2011). Anomaly detection in hyperspectral imagery using stable distribution .
SMART BIOMEDICAL AND PHYSIOLOGICAL SENSOR TECHNOLOGY XI,
8049 10.1117/12.884913
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Mercan, S, Alam, MS. (2011). Anomaly detection in hyperspectral imagery using stable distribution .
SMART BIOMEDICAL AND PHYSIOLOGICAL SENSOR TECHNOLOGY XI,
8049 10.1117/12.884913
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cited authors
Mercan, S; Alam, MS
authors
Mercan, Suat
abstract
In hyperspectral imaging applications, the background generally exhibits a clearly non-Gaussian impulsive behavior, where valuable information stays in the tail. In this paper, we propose a new technique, where the background is modeled using the stable distribution for robust detection of outliers. The outliers of the distribution can be considered as potential anomalies or regions of interests (ROIs). We effectively utilize the stable model for detecting targets in impulsive hyperspectral data. To decrease the false alarm rate, it is necessary to compare the ROI with the known reference using a suitable technique, such as the Euclidian distance. Modeling data with stable distribution compensates a drawback of the Gaussian model, which is not well suited for describing signals with impulsive behavior. In addition, thresholding is considered to avoid misclassification of targets. Test results using real life hyperspectral image datasets are presented to verify the effectiveness of the proposed technique. © 2011 SPIE.
publication date
June 29, 2011
published in
ADVANCED BIOMEDICAL AND CLINICAL DIAGNOSTIC SYSTEMS VII
Book
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Digital Object Identifier (DOI)
https://doi.org/10.1117/12.884913
Additional Document Info
volume
8049