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Proceedings Article

Methodology for hyperspectral image classification using novel neural network

[+] Author Affiliations
Suresh Subramanian, Nahum Gat, Michael Sheffield

Opto-Knowledge Systems, Inc. (USA)

Jacob Barhen

Oak Ridge National Lab. (USA)

Nikzad Toomarian

Jet Propulsion Lab. (USA)

Proc. SPIE 3071, Algorithms for Multispectral and Hyperspectral Imagery III, 128 (August 4, 1997); doi:10.1117/12.280589
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From Conference Volume 3071

  • Algorithms for Multispectral and Hyperspectral Imagery III
  • A. Evan Iverson; Sylvia S. Shen
  • Orlando, FL, USA | April 21, 1997

abstract

A novel feed forward neural network is used to classify hyperspectral data from the AVIRIS sensor. The network applies an alternating direction singular value decomposition technique to achieve rapid training times. Very few samples are required for training. 100 percent accurate classification is obtained using test data sets. The methodology combines this rapid training neural network together with data reduction and maximal feature separation techniques such as principal component analysis and simultaneous diagonalization of covariance matrices, for rapid and accurate classification of large hyperspectral images. The results are compared to those of standard statistical classifiers.

© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Suresh Subramanian ; Nahum Gat ; Michael Sheffield ; Jacob Barhen and Nikzad Toomarian
"Methodology for hyperspectral image classification using novel neural network", Proc. SPIE 3071, Algorithms for Multispectral and Hyperspectral Imagery III, 128 (August 4, 1997); doi:10.1117/12.280589; http://dx.doi.org/10.1117/12.280589


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