Neural and statistical techniques for remote sensing image classification
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 ne...
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| Date: | 2026 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Published: |
PROBLEMS IN PROGRAMMING
2026
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| Subjects: | |
| Online Access: | https://pp.isofts.kiev.ua/index.php/ojs1/article/view/956 |
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| Journal Title: | Problems in programming |
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Problems in programming| Summary: | 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.Problems in programming 2010; 2-3: 577-583 |
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