Techniques for the extraction of spatial and spectral information in the supervised classification of hyperspectral imagery for land-cover applications

  1. Álvaro Acción Montes
Dirixida por:
  1. Dora Blanco Heras Director
  2. Francisco Argüello Pedreira Director

Universidade de defensa: Universidade de Santiago de Compostela

Fecha de defensa: 05 de maio de 2023

  1. Raúl Celestino Guerra Fernández Presidente/a
  2. Natalia Seoane Iglesias Secretaria
  3. Javier Muro Martín Vogal
  1. Departamento de Electrónica e Computación

Tipo: Tese


The objective of this PhD thesis is the development of spatialspectral information extraction techniques for supervised classification tasks, both by means of classical models and those based on deep learning, to be used in the classification of land use or land cover (LULC) multi- and hyper-spectral images obtained by remote sensing. The main goal is the efficient application of these techniques, so that they are able to obtain satisfactory classification results with a low use of computational resources and low execution time.