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    Application on semantic segmentation with few labels in the detection of water bodies from PERUSAT-1 satellite's images

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    Acceso cerrado
    Date
    2020-07-02
    Author
    Gonzalez Villarreal, Jessenia Margareth Marina
    Acceso a Texto completo
    URI
    http://hdl.handle.net/20.500.12404/16610
    Abstract
    Remote sensing is widely used to monitor earth surfaces with the main objective of extracting information from it. Such is the case of water surface, which is one of the most affected extensions when flood events occur, and its monitoring helps in the analysis of detecting such affected areas, considering that adequately defining water surfaces is one of the biggest problems that Peruvian authorities are concerned with. In this regard, semi automatic mapping methods improve this monitoring, but this process remains a time-consuming task and into the subjectivity of the experts. In this work, we present a new approach for segmenting water surfaces from satellite images based on the application of convolutional neural networks. First, we explore the application of a U-Net model and then a transfer knowledge-based model. Our results show that both approaches are comparable when trained using an 680-labelled satellite image dataset; however, as the number of training samples is reduced, the performance of the transfer knowledge-based model, which combines high and very high image resolution characteristics, is improved
    Temas
    Sensores remotos
    Reconocimiento de imágenes
    Satélites artificiales
    Redes neuronales
    Collections
    • Informática con mención en Ciencias de la Computación

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