Moderate resolution imaging spectroradiometer products classification using deep learning
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.
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Subjects: | Sensores remotos, Aprendizaje profundo, Aprendizaje automático (Inteligencia artificial), Algoritmos, |
Online Access: | http://dx.doi.org/10.1007/978-3-030-33229-7_6 |
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KOHA-OAI-ECOSUR:599362024-03-11T15:25:26ZModerate resolution imaging spectroradiometer products classification using deep learning Arellano Verdejo, Javier Doctor autor 21598 textengDuring 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.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.Sensores remotosAprendizaje profundoAprendizaje automático (Inteligencia artificial)AlgoritmosTelematics 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.)http://dx.doi.org/10.1007/978-3-030-33229-7_6Disponible para usuarios de ECOSUR con su clave de acceso |
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Sensores remotos Aprendizaje profundo Aprendizaje automático (Inteligencia artificial) Algoritmos Sensores remotos Aprendizaje profundo Aprendizaje automático (Inteligencia artificial) Algoritmos Arellano Verdejo, Javier Doctor autor 21598 Moderate resolution imaging spectroradiometer products classification using deep learning |
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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. |
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Texto |
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Sensores remotos Aprendizaje profundo Aprendizaje automático (Inteligencia artificial) Algoritmos |
author |
Arellano Verdejo, Javier Doctor autor 21598 |
author_facet |
Arellano Verdejo, Javier Doctor autor 21598 |
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Arellano Verdejo, Javier Doctor autor 21598 |
title |
Moderate resolution imaging spectroradiometer products classification using deep learning |
title_short |
Moderate resolution imaging spectroradiometer products classification using deep learning |
title_full |
Moderate resolution imaging spectroradiometer products classification using deep learning |
title_fullStr |
Moderate resolution imaging spectroradiometer products classification using deep learning |
title_full_unstemmed |
Moderate resolution imaging spectroradiometer products classification using deep learning |
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moderate resolution imaging spectroradiometer products classification using deep learning |
url |
http://dx.doi.org/10.1007/978-3-030-33229-7_6 |
work_keys_str_mv |
AT arellanoverdejojavierdoctorautor21598 moderateresolutionimagingspectroradiometerproductsclassificationusingdeeplearning |
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