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

  1. Álvaro Acción Montes
Supervised by:
  1. Dora Blanco Heras Director
  2. Francisco Argüello Pedreira Director

Defence university: Universidade de Santiago de Compostela

Fecha de defensa: 05 May 2023

Committee:
  1. Raúl Celestino Guerra Fernández Chair
  2. Natalia Seoane Iglesias Secretary
  3. Javier Muro Martín Committee member
Department:
  1. Department of Electronics and Computing

Type: Thesis

Teseo: 798993 DIALNET lock_openMINERVA editor

Abstract

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.