@Article{AdarmeFeiHapAlmGom:2020:EvDeLe,
author = "Adarme, Mabel Ortega and Feitosa, Raul Queiroz and Happ, Patrick
Nigri and Almeida, Cl{\'a}udio Aparecido de and Gomes, Alessandra
Rodrigues",
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 {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Evaluation of deep learning techniques for deforestation detection
in the brazilian amazon and cerrado biomes from remote sensing
imagery",
journal = "Remote Sensing",
year = "2020",
volume = "12",
number = "6",
pages = "e910",
month = "Mar.",
note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 15: Vida terrestre}",
keywords = "deforestation detection, Brazilian biomes, deep learning, optical
imagery.",
abstract = "Deforestation is one of the major threats to natural ecosystems.
This process has a substantial contribution to climate change and
biodiversity reduction. Therefore, the monitoring and early
detection of deforestation is an essential process for
preservation. Techniques based on satellite images are among the
most attractive options for this application. However, many
approaches involve some human intervention or are dependent on a
manually selected threshold to identify regions that suffer
deforestation. Motivated by this scenario, the present work
evaluates Deep Learning-based strategies for automatic
deforestation detection, namely, Early Fusion (EF), Siamese
Network (SN), and Convolutional Support Vector Machine (CSVM) as
well as Support Vector Machine (SVM), used as the baseline. The
target areas are two regions with different deforestation
patterns: the Amazon and Cerrado biomes in Brazil. The experiments
used two co-registered Landsat 8 images acquired at different
dates. The strategies based on Deep Learning achieved the best
performance in our analysis in comparison with the baseline, with
SN and EF superior to CSVM and SVM. In the same way, a reduction
of the salt-and-pepper effect in the generated probabilistic
change maps was noticed as the number of training samples
increased. Finally, the work assesses how the methods can reduce
the time invested in the visual inspection of deforested areas.",
doi = "10.3390/rs12060910",
url = "http://dx.doi.org/10.3390/rs12060910",
issn = "2072-4292",
language = "en",
targetfile = "adarmel_evaluation.pdf",
urlaccessdate = "01 jun. 2024"
}