Radiometry, Infrared Systems, Tracking

Improved covariance matrices for point target detection in hyperspectral data

[+] Author Affiliations
Charlene E. Caefer

Air Force Research Laboratory, Sensors Directorate, Infrared Sensor Technology Branch, 80 Scott Drive, Hanscom Air Force Base, Massachusetts 01731

Jerry Silverman

Solid State Scientific Corporation, 27-2 Wright Road, Hollis, New Hampshire 03049

Oded Orthal

Ben-Gurion University of the Negev, Department of Electrical and Computer Engineering, Beer-Sheva, Israel

Dani Antonelli

Ben-Gurion University of the Negev, Department of Electrical and Computer Engineering, Beer-Sheva, Israel

Yaron Sharoni

Ben-Gurion University of the Negev, Department of Electrical and Computer Engineering, Beer-Sheva, Israel

Stanley R. Rotman

Solid State Scientific Corporation, 27-2 Wright Road, Hollis, New Hampshire 03049 and Ben-Gurion University of the Negev, Department of Electrical and Computer Engineering, Beer-Sheva, Israel

Opt. Eng. 47(7), 076402 (August 01, 2008). doi:10.1117/1.2965814
History: Received January 18, 2008; Revised May 02, 2008; Accepted May 11, 2008; Published August 01, 2008
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Our goals in hyperspectral point target detection have been to develop a methodology for algorithm comparison and to advance point target detection algorithms through the fundamental understanding of spatial and spectral statistics. In this paper, we review our methodology as well as present new metrics. We demonstrate improved performance by making better estimates of the covariance matrix. We have found that the use of covariance matrices of statistical stationary segments in the matched-filter algorithm improves the receiver operating characteristic curves; proper segment selection for each pixel should be based on its neighboring pixels. We develop a new type of local covariance matrix, which can be implemented in principal-component space and which also shows improved performance based on our metrics. Finally, methods of fusing the segmentation approach with the local covariance matrix dramatically improve performance at low false-alarm rates while maintaining performance at higher false-alarm rates.

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© 2008 Society of Photo-Optical Instrumentation Engineers

Citation

Charlene E. Caefer ; Jerry Silverman ; Oded Orthal ; Dani Antonelli ; Yaron Sharoni, et al.
"Improved covariance matrices for point target detection in hyperspectral data", Opt. Eng. 47(7), 076402 (August 01, 2008). ; http://dx.doi.org/10.1117/1.2965814


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