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Proceedings Paper

Anomaly detection of stationary targets in pan-sharpened IKONOS multispectral imagery
Author(s): Julian Meng; Andrew E. Marble; Yun Zhang; Jeff Secker
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Paper Abstract

Multispectral (MS) and hyperspectral (HS) sensors can facilitate target or anomaly detection in clutter since natural clutter and man-made objects diff er in the energy they radiate across the electromagnetic spectrum. Previous research in anomaly detection has formulated two popular algorithms: those based on Gauss-Markov Random Fields (GMRF) and the so-called RX-detector. Performance of these algorithms is dependent on a number of issues including spatial resolution, spectral correlation between the imaging bands, clutter/target model accuracy and the acquired data's signal-to-noise ratio (SNR). This paper provides a comparison study of the anomaly detection performance of the RXdetector and the GMRF-based algorithm using: (1) 4m MS imagery acquired f rom the IKONOS satellite and (2) pansharpened 1m MS imagery created by fusing the 4m MS and the associated 1m panchromatic image sets. The study will be based on the detection performance for stationary and slow moving targets selected f rom imagery acquired during training exercises at Canadian Forces Base (CFB) Petawawa and CFB Wainwright, Canada.

Paper Details

Date Published: 1 June 2005
PDF: 12 pages
Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.602706
Show Author Affiliations
Julian Meng, Univ. of New Brunswick (Canada)
Andrew E. Marble, Univ. of New Brunswick (Canada)
Yun Zhang, Univ. of New Brunswick (Canada)
Jeff Secker, Defence R&D Canada (Canada)


Published in SPIE Proceedings Vol. 5806:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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