Improved covariance matrices for point target detection in hyperspectral data

Charlene E. Caefer; Jerry Silverman; O. Orthal; D. Antonelli; Y. Sharoni; Stanley R. Rotman

doi:10.1117/1.2965814

1 July 2008 Improved covariance matrices for point target detection in hyperspectral data

Charlene E. Caefer, Jerry Silverman, O. Orthal, D. Antonelli, Y. Sharoni, Stanley R. Rotman

Author Affiliations +

Optical Engineering, Vol. 47, Issue 7, 076402 (July 2008). https://doi.org/10.1117/1.2965814

Abstract

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.

©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Charlene E. Caefer, Jerry Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and Stanley R. Rotman "Improved covariance matrices for point target detection in hyperspectral data," Optical Engineering 47(7), 076402 (1 July 2008). https://doi.org/10.1117/1.2965814

Published: 1 July 2008

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available