Improved covariance matrices for point target detection in hyperspectral data

Charlene E. Caefer, Jerry Silverman, Oded Orthal, Dani Antonelli, Yaron Sharoni, Stanley R. Rotman

Research output: Contribution to journalReview articlepeer-review

70 Scopus citations


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.

Original languageEnglish
Article number076402
JournalOptical Engineering
Issue number7
StatePublished - 1 Dec 2008


  • Mahalanobis distance
  • covariance matrix
  • hyperspectral
  • point target detection

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (all)


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