Open AccessCOMPARATIVE ANALYSIS OF NEURAL NETWORKS AND STATISTICAL APPROACHES TO REMOTE SENSING IMAGE CLASSIFICATION
Author(s) -
Nataliya N. Kussul,
Serhiy Skakun,
Olga Kussul
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.5.2.402
Subject(s) - artificial neural network , computer science , artificial intelligence , modular neural network , pattern recognition (psychology) , gaussian , classifier (uml) , multilayer perceptron , contextual image classification , satellite image , perceptron , modular design , statistical analysis , machine learning , data mining , image (mathematics) , satellite , time delay neural network , mathematics , statistics , engineering , physics , quantum mechanics , aerospace engineering , operating system
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.