Full Content is available to subscribers

Subscribe/Learn More  >
Proceedings Article

Hyperspectral change detection and supervised matched filtering based on covariance equalization

[+] Author Affiliations
Alan P. Schaum

Naval Research Lab. (USA)

Alan Stocker

Space Computer Corp. (USA)

Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, 77 (August 12, 2004); doi:10.1117/12.544026
Text Size: A A A
From Conference Volume 5425

  • Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X
  • Sylvia S. Shen; Paul E. Lewis
  • Orlando, FL | April 12, 2004

abstract

For hyperspectral remote sensing, the physics-based transformation connecting two multivariate sets of spectral radiance data of the same scene collected at two disparate times is approximately linear (plus an offset). Generally, the covariance structures of two such data sets provide partial information about any linear transformation connecting them. The remaining unknown degrees of freedom of the transformation must be deduced from other statistics, or from a knowledge of the underlying phenomenology. Among all the possible transformations consistent with measured pairs of hyperspectral covariance structures, a particularly simple and accurate one has been found. This "rotation free" flavor of "Covariance Equalization" (CE) has led to a simplified signal processing architecture that has been implemented in a real time VNIR hyperspectral target detection system. This paper describes that architecture, presents detection performance results, and introduces a new algorithm for long-interval change detection, Matched Change Detection.

© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Alan P. Schaum and Alan Stocker
"Hyperspectral change detection and supervised matched filtering based on covariance equalization", Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, 77 (August 12, 2004); doi:10.1117/12.544026; http://dx.doi.org/10.1117/12.544026


Access This Proceeding
Sign in or Create a personal account to Buy this proceeding ($15 for members, $18 for non-members).

Figures

Tables

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Proceeding
Sign in or Create a personal account to Buy this proceeding ($15 for members, $18 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.