Moderate resolution imaging spectroradiometer products classification using deep learning
Por: Arellano Verdejo, Javier. Doctor [autor]
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Tipo de material: ![Capítulo de libro
en línea](/opac-tmpl/lib/famfamfam/AR.png)
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Tipo de ítem | Biblioteca actual | Colección | Signatura | Estado | Fecha de vencimiento | Código de barras |
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Capítulos de libro | Biblioteca Electrónica Recursos en línea (RE) | ECOSUR | Recurso digital | ECO40059936454 |
Disponible para usuarios de ECOSUR con su clave de acceso
During the last years, the algorithms based on Artificial Intelligence have increased their popularity thanks to their application in multiple areas of knowledge. Nowadays with the increase of storage capacities and computing power, as well as the incorporation of new technologies for massively parallel processing (GPUs and TPUs) and Cloud Computing, it is increasingly common to incorporate this kind of algorithms and technology in tasks with a deep social and technological impact. In the present work a new Convolutional Neural Network specialized in the automatic classification of Moderate Resolution Imaging Spectroradiometer satellite products is proposed. The proposed architecture has shown a high-generalization by classifying more than 250,000 images with 99.99% accuracy. The methodology designed also can beextended, with other types of images, to make detection of Sargassum, oil spills, red tide, etc. eng