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Publikācija: Land Cover Classification Based On MODIS Imagery Data Using Artificial Neural Networks

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Nosaukums oriģinālvalodā Land Cover Classification Based On MODIS Imagery Data Using Artificial Neural Networks
Pētniecības nozare 2. Inženierzinātnes un tehnoloģijas
Pētniecības apakšnozare 2.2. Elektrotehnika, elektronika, informācijas un komunikāciju tehnoloģijas
Autori Artūrs Stepčenko
Atslēgas vārdi Artificial Neural Networks, Normalized Difference Vegetation Index, Pattern Recognition, Variational Mode Decomposition
Anotācija Remote sensing has been widely used to obtain land cover information using automated classification. Land cover is a measure of what is overlaying the surface of the earth. Accurate mapping of land cover on a regional scale is useful in such fields as precision agriculture or forest management and is one of the most important applications in remote sensing. In this study, multispectral MODIS Terra NDVI images and an artificial neural network (ANN) were used in land cover classification. Artificial neural network is a computing tool that is designed to simulate the way the human brain analyzes and process information. Artificial neural networks are one of the commonly applied machine learning algorithm, and they have become popular in the analysis of remotely sensed data, particularly in classification or feature extraction from image data more accurately than conventional method. This paper focuses on an automated classification system based on a pattern recognition neural network. Variational mode decomposition method is used as an image data pre-processing tool in this classification system. The result of this study will be land cover map.
DOI: 10.17770/etr2017vol2.2545
Hipersaite: http://journals.ru.lv/index.php/ETR/article/view/2545 
Atsauce Stepčenko, A. Land Cover Classification Based On MODIS Imagery Data Using Artificial Neural Networks. No: Vide. Tehnoloģija. Resursi : XI starptautiskās zinātniski praktiskās konferences materiāli = Environment. Technology. Resources : Proceedings of the 11th International Scientific and Practical Conference, Latvija, Rēzekne, 15.-17. jūnijs, 2017. Rēzekne: Rēzeknes Tehnoloģiju akadēmija, 2017, 2.sēj., 159.-164.lpp. ISSN 1691-5402. e-ISSN 2256-070X. Pieejams: doi:10.17770/etr2017vol2.2545
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ID 25650