Research Papers

Anomaly detection in hyperspectral imagery: comparison of methods using diurnal and seasonal data

[+] Author Affiliations
Patrick C. Hytla, Russell C. Hardie

University of Dayton, 300 College Park, Dayton, OH 45469-0187

Michael T. Eismann, Joseph Meola

Air Force Research Laboratory, 2241 Avionics Circle, Wright-Patterson AFB, OH 45433

J. Appl. Remote Sens. 3(1), 033546 (September 3, 2009). doi:10.1117/1.3236689
History: Received February 14, 2008; Revised June 29, 2009; Accepted August 26, 2009; September 3, 2009; Online September 03, 2009
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Abstract

The use of hyperspectral imaging is a fast growing field with many applications in the civilian, commercial and military sectors. Hyperspectral images are typically composed of many spectral bands in the visible and infrared regions of the electromagnetic spectrum and have the potential to deliver a great deal of information about a remotely sensed scene. One area of interest regarding hyperspectral images is anomaly detection, or the ability to find spectral outliers within a complex background in a scene with no a priori information about the scene or its specific contents. Anomaly detectors typically operate by creating a statistical background model of a hyperspectral image and measuring anomalies as image pixels that do not conform properly to that given model. In this study we compare the performance over diurnal and seasonal changes for several different anomaly detection methods found in the literature and a new anomaly detector that we refer to as the fuzzy cluster-based anomaly detector. Here we also compare the performance of several anomaly-based change detection algorithms. Our results indicate that all anomaly detectors tested in this experimentation exhibit strong performance under optimum illumination and environmental conditions. However, our results point toward a significant performance advantage for cluster-based anomaly detectors in the presence of adverse environmental conditions.

© 2009 Society of Photo-Optical Instrumentation Engineers

Citation

Patrick C. Hytla ; Russell C. Hardie ; Michael T. Eismann and Joseph Meola
"Anomaly detection in hyperspectral imagery: comparison of methods using diurnal and seasonal data", J. Appl. Remote Sens. 3(1), 033546 (September 3, 2009). ; http://dx.doi.org/10.1117/1.3236689


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