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3 March 2008 Joint fusion and detection of mines using hyperspectral and SAR data
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Proceedings Volume 6812, Image Processing: Algorithms and Systems VI; 68120U (2008)
Event: Electronic Imaging, 2008, San Jose, California, United States
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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nasser M. Nasrabadi "Joint fusion and detection of mines using hyperspectral and SAR data", Proc. SPIE 6812, Image Processing: Algorithms and Systems VI, 68120U (3 March 2008);

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