@Article{CamargoSanAlmMurAlm:2019:CoAsMa,
author = "Camargo, Fl{\'a}vio F. and Sano, Edson E. and Almeida,
Cl{\'a}udia Maria de and Mura, Jos{\'e} Cl{\'a}udio and
Almeida, Tati",
affiliation = "{Universidade de Bras{\'{\i}}lia (UnB)} and {Embrapa Cerrados}
and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade de Bras{\'{\i}}lia (UnB)}",
title = "A comparative assessment of machine-learning techniques for land
use and land cover classification of the Brazilian tropical
savanna using ALOS-2/PALSAR-2 polarimetric images",
journal = "Remote Sensing",
year = "2019",
volume = "11",
number = "13",
pages = "e1600",
month = "July",
note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 15: Vida terrestre}",
keywords = "SAR, polarimetry, data mining, thematic mapping, Cerrado.",
abstract = "This study proposes a workflow for land use and land cover (LULC)
classification of Advanced Land Observing Satellite-2 (ALOS-2)
Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2)
images of the Brazilian tropical savanna (Cerrado) biome. The
following LULC classes were considered: forestlands; shrublands;
grasslands; reforestations; croplands; pasturelands; bare
soils/straws; urban areas; and water reservoirs. The proposed
approach combines polarimetric attributes, image segmentation, and
machine-learning procedures. A set of 125 attributes was generated
using polarimetric ALOS-2/PALSAR-2 images, including the van Zyl,
Freeman- Durden, Yamaguchi, and Cloude-Pottier target
decomposition components, incoherent polarimetric parameters
(biomass indices and polarization ratios), and HH-, HV-, VH-,
andVV-polarized amplitude images. These attributes were classified
using the Naive Bayes (NB), DT J48 (DT = decision tree), Random
Forest (RF), Multilayer Perceptron (MLP), and Support Vector
Machine (SVM) algorithms. The RF, MLP, and SVM classifiers
presented the most accurate performances. NB and DT J48
classifiers showed a lower performance in relation to the RF, MLP,
and SVM. The DT J48 classifier was the most suitable algorithm for
discriminating urban areas and natural vegetation cover. The
proposed workflow can be replicated for other SAR images with
different acquisition modes or for other types of vegetation
domains.",
doi = "10.3390/rs11131600",
url = "http://dx.doi.org/10.3390/rs11131600",
issn = "2072-4292",
language = "en",
targetfile = "remotesensing-11-01600.pdf",
urlaccessdate = "27 abr. 2024"
}