@InProceedings{OrtegaBerHapGomFei:2019:EvDeLe,
author = "Ortega, M. X. and Bermudez, J. D. and Happ, P. Nigri and Gomes,
Alessandra Rodrigues and Feitosa, R. Queiroz",
affiliation = "{Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro
(PUC-Rio)} and {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do
Rio de Janeiro (PUC-Rio)} and {Pontif{\'{\i}}cia Universidade
Cat{\'o}lica do Rio de Janeiro (PUC-Rio)} and {Instituto Nacional
de Pesquisas Espaciais (INPE)} and {Pontif{\'{\i}}cia
Universidade Cat{\'o}lica do Rio de Janeiro (PUC-Rio)}",
title = "Evaluation of deep learning techniques for deforestation detection
in the Amazon forest",
booktitle = "Proceedings...",
year = "2019",
organization = "Photogrammetric Image Analysis",
note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 15: Vida terrestre}",
abstract = "Deforestation is one of the main causes of biodiversity reduction,
climate change among other destructive phenomena. Thus, early
detection of deforestation processes is of paramount importance.
Motivated by this scenario, this work presents an evaluation of
methods for automatic deforestation detection, specifically Early
Fusion (EF) Convolutional Network, Siamese Convolutional Network
(S-CNN) and the well-known Support Vector Machine (SVM), taken as
the baseline. These methods were evaluated in a region of the
Brazilian Legal Amazon (BLA). Two Landsat 8 images acquired in
2016 and 2017 were used in our experiments. The impact of training
set size was also investigated. The Deep Learning-based approaches
clearly outperformed the SVM baseline in our approaches, both in
terms of F1-score and Overall Accuracy, with a superiority of
S-CNN over EF.",
conference-location = "Munich",
conference-year = "18-20 Sept.",
language = "en",
targetfile = "ortega_evaluatio.pdf",
urlaccessdate = "20 set. 2024"
}