@Article{DinizGamaAdam:2020:EvPoIn,
author = "Diniz, Juliana Maria Ferreira de Souza and Gama, F{\'a}bio Furlan
and Adami, Marcos",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Evaluation of polarimetry and interferometry of Sentinel-1A SAR
data for land use and land cover of the Brazilian Amazon region",
journal = "Geocarto International",
year = "2020",
volume = "35",
pages = "e1773544",
keywords = "Dual-polarimetric, interferometric coherence, machine learning
algorithms, land cover mapping.",
abstract = "Synthetic aperture radar (SAR) data has been an alternative for
monitoring ground targets, especially in areas with cloud cover.
This study evaluates the potential of Sentinel-1A attributes for
mapping land use and land cover (LULC) in a region of the
Brazilian Amazon, using two different machine learning
classifiers: Random Forest (RF) and Support Vector Machine (SVM).
Different scenarios were used that combined backscattering,
polarimetry, and interferometry to the classification process,
which was divided into two phases to improve the results. The RF
shows superiority over the SVM for almost all scenarios for the
two phases of the mapping. The scenario with all data, presented
the best results with both classifiers. The final maps with RF and
SVM, obtained a global accuracy of 82.7% and 74.5%, respectively.
This study demonstrated the potential of Sentinel-1 to map LULC
classes in the Amazon region using a classification in two
phases.",
doi = "10.1080/10106049.2020.1773544",
url = "http://dx.doi.org/10.1080/10106049.2020.1773544",
issn = "1010-6049",
label = "lattes: 7484071887086439 3 DinizGamaAdam:2020:EvPoIn",
language = "en",
targetfile = "diniz_evaluatin.pdf",
urlaccessdate = "28 abr. 2024"
}