Methodology for hyperspectral image classification using novel neural network

Suresh Subramanian; Nahum Gat; Michael Sheffield; Jacob Barhen; Nikzad Toomarian

doi:10.1117/12.280589

4 August 1997 Methodology for hyperspectral image classification using novel neural network

Suresh Subramanian, Nahum Gat, Michael Sheffield, Jacob Barhen, Nikzad Toomarian

Proceedings Volume 3071, Algorithms for Multispectral and Hyperspectral Imagery III; (1997) https://doi.org/10.1117/12.280589
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download 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, (4 August 1997); https://doi.org/10.1117/12.280589

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