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Memorias de investigación
Book chapters:
Deep Learning for superpixel-based classfication of remote sensing images.
Year:2016
Research Areas
  • Remote sensing
Information
Abstract
Recently deep learning-based methods have demonstrated excellent performance on different artificial-intelligence tasks. Even though, in the last years, several related works are found in the literature in the remote sensing field, a small percentage of them address the classification problem. These works propose schemes based on image patches to perform pixel-based image classification. Due to the typical remote sensing image size, the main drawback of these schemes is the time required by the window-sliding process implied in them. In this work, we propose a strategy to reduce the time spent on the classification of a new image through the use of superpixel segmentation. Several experiments using CNNs trained with different sizes of patches and superpixels have been performed on the ISPRS semantic labeling benchmark. Obtained results show that while the accuracy of the classification carried out by using superpixels is similar to the results generated by pixel-based approach, the expended time is dramatically decreased by means of reducing the number of elements to label.
International
Si
Book Edition
Book Publishing
6th GEOBIA Conference
ISBN
Series
Book title
GEOBIA 2016: solutions and synergies
From page
To page
Participants
  • Autor: Consuelo Gonzalo Martin (UPM)
  • Autor: Angel Mario García Pedrero (UPM)
  • Autor: Mario Lillo Saavedra (Universidad de Concepción)
  • Autor: Ernestina Menasalvas Ruiz (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Minería de Datos y Simulación (MIDAS)
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