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Moderate resolution imaging spectroradiometer products classification using deep learning

Por: Arellano Verdejo, Javier. Doctor [autor].
Tipo de material: Capítulo de libro en línea Capítulo de libro en línea Tipo de contenido: Texto Tipo de medio: Computadora Tipo de portador: Recurso en líneaTema(s): Sensores remotos | Aprendizaje profundo | Aprendizaje automático (Inteligencia artificial) | AlgoritmosTema(s) en inglés: Remote sensing | Deep learning | Machine learning | AlgorithmsNota de acceso: Disponible para usuarios de ECOSUR con su clave de acceso En: Telematics and computing: 8th international congress, WITCOM 2019 Merida, Mexico, November 4–8, 2019 proceedings / Miguel Felix Mata-Rivera, Roberto Zagal-Flores, Cristian Barría-Huidobro (Eds.). Cham, Switzerland : Springer Nature Switzerland AG, 2019. páginas 61–70. --ISBN: 978-3-030-33228-0, 978-3-030-33229-7 (eBook)Número de sistema: 59936Resumen:
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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.

Recurso en línea: http://dx.doi.org/10.1007/978-3-030-33229-7_6
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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

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