Deep Learning Based Classification Techniques for Hyperspectral Images in Real Time

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...

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Detalhes bibliográficos
Autor: Suárez Garea, Jorge Alberto
Tipo de documento: tese
Data de publicação:2021
País:España
Recursos:Universidad de Santiago de Compostela (USC)
Repositório:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Idioma:inglês
OAI Identifier:oai:minerva.usc.gal:10347/27005
Acesso em linha:http://hdl.handle.net/10347/27005
Access Level:Acceso aberto
Palavra-chave:Materias::Investigación::33 Ciencias tecnológicas::3304 Tecnología de los ordenadores::330406 Arquitectura de ordenadores
Descrição
Resumo: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.

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