Hyperspectral data due to large number of spectral bands facilitates discrimination between large numbers of classes in a data; however, the advantage afforded by the hyperspectral data often tends to get lost in the limitations of convection al classifier techniques. Artificial Neural Networks (ANN) in several studies has shown to outperform convection al classifiers, however; there are several issues with regard to selection of parameters for achieving best possible classification accuracy. Objectives of this study have been accordingly formulated to include an investigation of t he effect of various Neural Network parameters on the accuracy of hyperspectral image classification. AVIRIS Hyperspectral Indian Pine Test site 3 dataset acquiredin220 Bands on June 12, 1992 has been used in the stud y. Thereafter, maximal feature extraction technique of Principle component analysis (PCA) is used to reduce the dataset t o 10 bands preserving of 99.96% variance. The data contains 16 major classes of which 4 have been considered for ANN based classification. The parameters selected for the study are – number of hidden layers, hidden Nodes, training sample size, learning rate and learning momentum. Backpropagation method of learning is adopted. The overall accuracy of the network trained has been assessed using test sample size of 300 pixels. Although, the study throws up certain distinct ranges within which higher classification accuracies can be expected, however, no definite relationship could be identified between various ANN parameters under study.

Date Published: 17 May 2016
PDF: 15 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 984003 (17 May 2016); doi: 10.1117/12.2223805

Published in SPIE Proceedings Vol. 9840:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Miguel Velez-Reyes; David W. Messinger, Editor(s)