Abstract
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 be extended, with other types of images, to make detection of Sargassum, oil spills, red tide, etc.
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Arellano-Verdejo, J. (2019). Moderate Resolution Imaging Spectroradiometer Products Classification Using Deep Learning. In: Mata-Rivera, M., Zagal-Flores, R., Barría-Huidobro, C. (eds) Telematics and Computing. WITCOM 2019. Communications in Computer and Information Science, vol 1053. Springer, Cham. https://doi.org/10.1007/978-3-030-33229-7_6
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DOI: https://doi.org/10.1007/978-3-030-33229-7_6
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