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.
Highlights
Recent advances in technologies made it possible to develop new satellite sensors with considerably improved parameters and characteristics
Models are trained on all subsets except for one, and classification rate is estimated by testing it on subset left out
In this paper we examined different neural networks models, in particular multilayer perceptron (MLP) and ARTMAP networks, and statistical approach, namely maximum likelihood method, for classification of remote sensing images
Summary
Recent advances in technologies made it possible to develop new satellite sensors with considerably improved parameters and characteristics. The use of such space-borne satellite sensors enables acquisition of valuable data that can be efficiently used for various applied problems solving in agriculture, natural resources monitoring, land use management, environmental monitoring, etc. Land cover classification represent one of the most important and typical applications of remote sensing data. Land cover corresponds to the physical condition of the ground surface, for example, forest, grassland, artificial surfaces etc. To this end, various approaches have been proposed, among which the most popular are neural networks [1] and statistical [2] methods
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