Full Content is available to subscribers

Subscribe/Learn More  >
Proceedings Article

Dynamic thresholding for hyperspectral shadow detection using Levenberg-Marquardt minimization on multiple Gaussian illumination distributions

[+] Author Affiliations
Brian D. Wemett, Jonathan K. Riek

VirtualScopics, Inc. (USA)

Robert A. Leathers

Naval Research Lab. (USA)

Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 733411 (April 27, 2009); doi:10.1117/12.817826
Text Size: A A A
From Conference Volume 7334

  • Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
  • Sylvia S. Shen; Paul E. Lewis
  • Orlando, Florida, USA | April 13, 2009

abstract

Irregular illumination across a hyperspectral image makes it difficult to detect targets in shadows, perform change detection, and segment the contents of the scene. To correct for the data in shadow, we first convert the data from Cartesian space to a hyperspherical coordinate system. Each N-dimensional spectral vector is converted to N-1 spectral angles and a magnitude representing the illumination value of the spectra. Similar materials will have similar angles and the differences in illumination will be described mostly by the magnitude. In the data analyzed, we found that the distribution of illumination values is well approximated by the sum of two- Gaussian distributions, one for shadow and one for non-shadow. The Levenberg-Marquardt algorithm is used to fit the empirical illumination distribution to the theoretical Gaussian sum. The LM algorithm is an iterative technique that locates the minimum of a multivariate function that is expressed as the sum of squares of non-linear real-valued functions. Once the shadow and non-shadow distributions have been modeled, we find the optimal point to be one standard deviation out on the shadow distribution, allowing for the selection of about 84% of the shadows. This point is then used as a threshold to decide if the pixel is shadow or not. Corrections are made to the shadow regions and a spectral matched filter is applied to the image to test target detection in shadow regions. Results show a signal-to-noise gain over other illumination suppression techniques.

© (2009) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Brian D. Wemett ; Jonathan K. Riek and Robert A. Leathers
"Dynamic thresholding for hyperspectral shadow detection using Levenberg-Marquardt minimization on multiple Gaussian illumination distributions", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 733411 (April 27, 2009); doi:10.1117/12.817826; http://dx.doi.org/10.1117/12.817826


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.