Kernel-based joint fusion/detection of anomalies using hyperspectral and SAR imagery

Nasser M. Nasrabadi

doi:10.1117/12.817701

27 April 2009 Kernel-based joint fusion/detection of anomalies using hyperspectral and SAR imagery

Nasser M. Nasrabadi

Proceedings Volume 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV; 73341B (2009) https://doi.org/10.1117/12.817701
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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 implicitly 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.

Citation Download Citation

Nasser M. Nasrabadi "Kernel-based joint fusion/detection of anomalies using hyperspectral and SAR imagery", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341B (27 April 2009); https://doi.org/10.1117/12.817701

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