CFP last date
20 June 2024
Reseach Article

Performance Analysis of ANN for Satellite Image Pixel Classification

by Agrawal Rajesh K., Bawane N. G.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 104 - Number 17
Year of Publication: 2014
Authors: Agrawal Rajesh K., Bawane N. G.
10.5120/18297-6534

Agrawal Rajesh K., Bawane N. G. . Performance Analysis of ANN for Satellite Image Pixel Classification. International Journal of Computer Applications. 104, 17 ( October 2014), 5-8. DOI=10.5120/18297-6534

@article{ 10.5120/18297-6534,
author = { Agrawal Rajesh K., Bawane N. G. },
title = { Performance Analysis of ANN for Satellite Image Pixel Classification },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 104 },
number = { 17 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 5-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume104/number17/18297-6534/ },
doi = { 10.5120/18297-6534 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:36:23.408222+05:30
%A Agrawal Rajesh K.
%A Bawane N. G.
%T Performance Analysis of ANN for Satellite Image Pixel Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 104
%N 17
%P 5-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a thorough experimental analysis to investigate the behavior of neural network classifier for classification of multispectral satellite images. For this series of experiments have been performed to study the effect of various neural network parameters upon classification accuracy. It is per pixel supervised classification using spectral bands (original feature space). The parameters considered are: initial weight, training set size, number of hidden layer neurons and number of input layer nodes. Based on 1050 number of experiments, it is concluded that for good classification accuracy and speed, following two critical issues needs to be addressed: 1) selection of most discriminative spectral bands and 2) determination of optimal number of nodes in hidden layer. The accuracy obtained with ANN classifier is compared with that of traditional classifiers like MLC and Euclidean classifier using Xie-Beni and ? indexes.

References
  1. P. M. Mather, Computer Processing of Remotely Sensed Images, John Wiley, UK, 1999.
  2. P. M. Atkinson, Tattnall, Neural network in remote sensing, Int. J. Remote Sensing 18 (4) (1997) 699-709.
  3. J. A. Benediktsson, Swain P. H. , Erosy O. K. , Neural network approaches versus statistical methods in classification of multisource remote sensing data, IEEE Transactions on Geosciences and Remote Sensing 28 (1990) 540-552.
  4. G. M. Foody, M. K. Arora, An evaluation of some factors affecting the accuracy of classification by an artificial neural network, Int. J. Remote Sensing 18 (4) (1997) 799–810.
  5. H. M. Chee, O. K. Ersoy, A statistical self-organizing learning system for remote sensing classification, IEEE Transactions on Geosciences and Remote Sensing 432 (8) (2005) 1890–1900.
  6. M. Acharyya, R. K. De, M. K. Kundu, Segmentation of remotely sensed images using wavelet features and their evaluation in soft computing framework, IEEE Transactions on Geosciences and Remote Sensing 41 (12) (2003) 2900-2905.
  7. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, England, 1997.
  8. R. C. Gonzalez, R. E. Woods, S. L. Eddins, Digital Image Processing using MATLAB, Pearson, Singapore, 2002.
  9. C. H. Chen, P. G. Peter Ho, Statistical pattern recognition in remote sensing, Pattern Recognition 41 (9) (2008) 2731–2741
  10. S. K. Meher, B. Uma Shankar, A. Ghosh, Wavelet-feature-based classifiers for multispectral remote-sensing images, IEEE Transactions on Geosciences and Remote Sensing 45 (6) (2007) 1881–1886.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Network Land Cover Classification Multispectral Satellite Imagery Neural Network Structure.