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Proceedings Paper

Methodology for hyperspectral image classification using novel neural network
Author(s): Suresh Subramanian; Nahum Gat; Michael Sheffield; Jacob Barhen; Nikzad Toomarian
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Paper 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.

Paper Details

Date Published: 4 August 1997
PDF: 10 pages
Proc. SPIE 3071, Algorithms for Multispectral and Hyperspectral Imagery III, (4 August 1997); doi: 10.1117/12.280589
Show Author Affiliations
Suresh Subramanian, Opto-Knowledge Systems, Inc. (United States)
Nahum Gat, Opto-Knowledge Systems, Inc. (United States)
Michael Sheffield, Opto-Knowledge Systems, Inc. (United States)
Jacob Barhen, Oak Ridge National Lab. (United States)
Nikzad Toomarian, Jet Propulsion Lab. (United States)

Published in SPIE Proceedings Vol. 3071:
Algorithms for Multispectral and Hyperspectral Imagery III
A. Evan Iverson; Sylvia S. Shen, Editor(s)

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