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dc.contributor.authorKussul, Nataliya
dc.contributor.authorSkakun, Serhiy
dc.contributor.authorKussul, OIga
dc.coverage.spatialБрестru_RU
dc.date.accessioned2023-11-24T06:27:23Z
dc.date.available2023-11-24T06:27:23Z
dc.date.issued2006
dc.identifier.citationKussul, N. Comparative Analysis of Neural Networks and Statistical Approaches to Remote Sensing Image Classification / Nataliya Kussul, Serhiy Skakun, Olga Kussul // International Conference on Neural Networks and Artificial Intelligence : proceedings, Brest, 31 May – 2 June, 2006 / Edited: V. Golovko [et al.]. – Brest : BSTU, 2006. – P. 175–181 : il. – Bibliogr.: p. 180–181 (23 titles).ru_RU
dc.identifier.urihttps://rep.bstu.by/handle/data/37189
dc.descriptionКуссуль Наталья, Скакун Сергей, Куссуль Ольга. Сравнительный анализ нейронных сетей и статистических подходов к классификации изображений дистанционного зондированияru_RU
dc.description.abstractThis paper examines different approaches to remote sensing images classification. Included in the study are statistical approach, namely Gaussian maximum likelihood classifier, and two different neural networks paradigms: multilayer pcreeptron 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.ru_RU
dc.language.isoruru_RU
dc.publisherBrSTUru_RU
dc.subjectremote sensing image classificationru_RU
dc.subjectклассификация изображений дистанционного зондированияru_RU
dc.subjectneural networksru_RU
dc.subjectнейронные сетиru_RU
dc.subjectstatistical methodsru_RU
dc.subjectстатистические методыru_RU
dc.subjectLandsat-7 satelliteru_RU
dc.subjectспутник Landsat-7ru_RU
dc.titleComparative Analysis of Neural Networks and Statistical Approaches to Remote Sensing Image Classificationru_RU
dc.title.alternativeСравнительный анализ нейронных сетей и статистических подходов к классификации изображений дистанционного зондированияru_RU
dc.typeНаучный доклад (Working Paper)ru_RU


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