Full Content is available to subscribers

Subscribe/Learn More  >
Proceedings Article

Waveband selection for hyperspectral data: optimal feature selection

[+] Author Affiliations
David P. Casasent

Carnegie Mellon Univ. (USA)

Xue-Wen Chen

California State Univ. (USA)

Proc. SPIE 5106, Optical Pattern Recognition XIV, 259 (August 6, 2003); doi:10.1117/12.501416
Text Size: A A A
From Conference Volume 5106

  • Optical Pattern Recognition XIV
  • David P. Casasent; Tien-Hsin Chao
  • Orlando, FL | April 21, 2003

abstract

Hyperspectral (HS) data contains spectral response information that provides detailed chemical, moisture, and other descriptions of constituent parts of an item. These new sensor data are useful in USDA product inspection and in automatic target recognition (ATR) applications. However, such data introduces problems such as the curse of dimensionality, the need to reduce the number of features used to accommodate realistic small training set sizes, and the need to employ discriminatory features and still achieve good generalization (comparable training and test set performance). HS produces high-dimensional data; this is characterized by a training set size (Ni) per class that is less than the number of input features (HS λ bands). A new high-dimensional generalized discriminant (HDGD) feature extraction algorithm and a new high-dimensional branch and bound (HDBB) feature selection algorithm are described and compared to other feature reduction methods for two HS product inspection applications. Cross-validation methods, not using the test set, select algorithm parameters.

© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

David P. Casasent and Xue-Wen Chen
"Waveband selection for hyperspectral data: optimal feature selection", Proc. SPIE 5106, Optical Pattern Recognition XIV, 259 (August 6, 2003); doi:10.1117/12.501416; http://dx.doi.org/10.1117/12.501416


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.