Deep Learning Based Classification Techniques for Hyperspectral Images in Real Time

  1. Alberto Suárez Garea
Dirixida por:
  1. Francisco Argüello Pedreira Director
  2. Dora Blanco Heras Director

Universidade de defensa: Universidade de Santiago de Compostela

Ano de defensa: 2021

  1. Jorge Azorín López Presidente/a
  2. José Ramón Ríos Viqueira Secretario
  3. Begüm Demir Vogal
  1. Departamento de Electrónica e Computación

Tipo: Tese


Remote sensing can be defined as the acquisition of information from a given scene without coming into physical contact with it, through the use of sensors, mainly located on aerial platforms, which capture information in different ranges of the electromagnetic spectrum. The objective of this thesis is the development of efficient schemes, based on the use of deep learning neural networks, for the classification of remotely sensed multi and hyperspectral land cover images. Efficient schemes are those that are capable of obtaining good results in terms of classification accuracy and that can be computed in a reasonable amount of time depending on the task performed. Regarding computational platforms, multicore architectures and Graphics Processing Units (GPUs) will be considered.