@Article{FerrariFerrAlmeFeit:2023:FuSeSe,
author = "Ferrari, Felipe and Ferreira, Matheus Pinheiro and Almeida,
Cl{\'a}udio Aparecido de and Feitosa, Raul Queiroz",
affiliation = "{Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro
(PUC-Rio)} and {Instituto Militar de Engenharia (IME)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Fusing Sentinel-1 and Sentinel-2 images for deforestation
detection under diverse cloud conditions",
journal = "IEEE Geoscience and Remote Sensing Letters",
year = "2023",
volume = "20",
pages = "e2501005",
keywords = "Adaptive optics, Biomedical optical imaging, Clouds, Land Surface,
Optical Data, Optical imaging, Optical sensors, Radar polarimetry,
SAR Data, Synthetic aperture radar, Vegetation.",
abstract = "Most current early warning systems for deforestation rely on
cloud-free optical images, which are difficult to obtain in
tropical regions. The fusion of optical and SAR images is an
attractive alternative in these cases. Although less
discriminative in cloudless regions, SAR data are nearly unaltered
by clouds, allowing better discrimination in cloudy areas than the
optical counterpart. This letter proposes solutions that seek the
best combination between the two modalities for each pixel as a
function of the surrounding cloud cover to maximize classification
accuracy. We compared early, joint, and late fusion variants of
Fully Convolutional Networks (FCN) to detect deforestation in the
Amazon rainforest from Sentinel 1 and Sentinel 2 data. Experiments
conducted to compare the architecture variants showed that
optical-SAR fusion might outperform the single-modal variants for
deforestation detection on pixels affected by any cloud cover
level. In particular, the joint fusion approach outperformed the
single modal counterparts under all cloud cover scenarios.",
doi = "10.1109/LGRS.2023.3242430",
url = "http://dx.doi.org/10.1109/LGRS.2023.3242430",
issn = "1545-598X",
language = "en",
targetfile = "
Fusing_Sentinel-1_and_Sentinel-2_Images_for_Deforestation_Detection_in_the_Brazilian_Amazon_Under_Diverse_Cloud_Conditions.pdf",
urlaccessdate = "04 maio 2024"
}