
Proceedings Paper
Joint fusion and detection of mines using hyperspectral and SAR dataFormat | Member Price | Non-Member Price |
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Paper Abstract
This paper describes a new nonlinear joint fusion and anomaly detection technique for mine detection applications using
two different types of sensor data (synthetic aperture radar (SAR) and Hyperspectral sensor (HS) data). A well-known
anomaly detector so called the RX algorithm is first extended to perform fusion and detection simultaneously at the pixel
level by appropriately concatenating the information from the two sensors. This approach is then extended to its
nonlinear version. The nonlinear fusion-detection approach is based on the statistical kernel learning theory which
explicitly exploits the higher order dependencies (nonlinear relationships) between the two sensor data through an
appropriate kernel. Experimental results for detecting anomalies (mines) in hyperspectral imagery are presented for
linear and nonlinear joint fusion and detection for a co-registered SAR and HS imagery. The result show that the
nonlinear techniques outperform linear versions.
Paper Details
Date Published: 3 March 2008
PDF: 8 pages
Proc. SPIE 6812, Image Processing: Algorithms and Systems VI, 68120U (3 March 2008); doi: 10.1117/12.771188
Published in SPIE Proceedings Vol. 6812:
Image Processing: Algorithms and Systems VI
Jaakko T. Astola; Karen O. Egiazarian; Edward R. Dougherty, Editor(s)
PDF: 8 pages
Proc. SPIE 6812, Image Processing: Algorithms and Systems VI, 68120U (3 March 2008); doi: 10.1117/12.771188
Show Author Affiliations
Nasser M. Nasrabadi, Army Research Lab. (United States)
Published in SPIE Proceedings Vol. 6812:
Image Processing: Algorithms and Systems VI
Jaakko T. Astola; Karen O. Egiazarian; Edward R. Dougherty, Editor(s)
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