COMPARATIVE ANALYSIS OF NEURAL NETWORKS AND STATISTICAL APPROACHES TO REMOTE SENSING IMAGE CLASSIFICATION

Authors

  • Nataliya Kussul
  • Serhiy Skakun
  • Olga Kussul

DOI:

https://doi.org/10.47839/ijc.5.2.402

Keywords:

Remote sensing image classification, neural networks, statistical methods, Landsat-7 satellite

Abstract

This paper examines different approaches to remote sensing images classification. Included in the study are statistical approach, in particular Gaussian maximum likelihood classifier, and two different neural networks paradigms: multilayer perceptron trained with EDBD algorithm, and ARTMAP neural network. These classification methods are compared on data acquired from Landsat-7 satellite. Experimental results showed that to achieve better performance of classifiers modular neural networks and committee machines should be applied.

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Published

2014-08-01

How to Cite

Kussul, N., Skakun, S., & Kussul, O. (2014). COMPARATIVE ANALYSIS OF NEURAL NETWORKS AND STATISTICAL APPROACHES TO REMOTE SENSING IMAGE CLASSIFICATION. International Journal of Computing, 5(2), 93-99. https://doi.org/10.47839/ijc.5.2.402

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Articles