Performance analysis of deep convolutional autoencoders with different patch sizes for change detection from burnt areas

Fire is one of the primary sources of damages to natural environments globally. Estimates show that approximately 4 million km2 of land burns yearly. Studies have shown that such estimates often underestimate the real extent of burnt land, which highlights the need to find better, state-of-the-art m...

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Detalhes bibliográficos
Autor principal: Bem, Pablo Pozzobon de (author)
Outros Autores: Carvalho Júnior, Osmar Abílio de (author), Carvalho, Osmar Luiz Ferreira de (author), Gomes, Roberto Arnaldo Trancoso (author), Guimarães, Renato Fontes (author)
Formato: article
Publicado em: 2022
Assuntos:
Aprendizagem profunda
Redes neurais (Computação)
Classificação
Fogo
Imagem multitemporal
Texto completo:https://doi.org/BEM, Pablo Pozzobon de et al. Performance analysis of deep convolutional autoencoders with different patch sizes for change detection from burnt areas. Remote Sensing, v. 12, n. 16, 2576, 2020. DOI: https://doi.org/10.3390/rs12162576. Disponível em: https://www.mdpi.com/2072-4292/12/16/2576. Acesso em: 16 maio 2022.
País:Brasil
Oai:oai:repositorio.unb.br:10482/43715

Texto Completo

https://doi.org/BEM, Pablo Pozzobon de et al. Performance analysis of deep convolutional autoencoders with different patch sizes for change detection from burnt areas. Remote Sensing, v. 12, n. 16, 2576, 2020. DOI: https://doi.org/10.3390/rs12162576. Disponível em: https://www.mdpi.com/2072-4292/12/16/2576. Acesso em: 16 maio 2022.

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