@Article{PereiraFreiSantReis:2018:EvOpRa,
author = "Pereira, Luciana O. and Freitas, Corina C. and Sant'Anna, Sidnei
Jo{\~a}o Siqueira and Reis, Mariane Souza",
affiliation = "{University of Exeter} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Evaluation of Optical and Radar Images Integration Methods for
LULC Classification in Amazon Region",
journal = "IEEE Journal of Selected Topics in Applied Earth Observations and
Remote Sensing",
year = "2018",
volume = "11",
number = "9",
pages = "3062--3074",
month = "sept.",
keywords = "—Brazilian Amazon, data integration, land-use and land-cover
(LULC), multipolarized-synthetic aperture radar (SAR).",
abstract = "The main objective of this study is to evaluate different methods
to integrate (fusion and combination) Synthetic Aperture Radar
(SAR) Advanced Land Observing Satellite (ALOS) Phased Arrayed
L-band SAR (PALSAR-1) (Fine Beam Dual mode-FDB) and LANDSAT images
in order to identify those which lead to higher accuracy of
land-use and land-cover (LULC) mapping in an agricultural frontier
region in Amazon. One method used to integrate the multipolarized
information in SAR images before the fusion process was also
evaluated. In this method, the first principal component (PC1 ) of
SAR data was used. Color compositions of fused data that presented
better LULC classification were visually analyzed. Considering the
proposed objective, the following fusion methods must be
highlighted: Ehlers, Wavelet a´ trous, Intensity, Hue and
Saturation (IHS), and selective principal component analysis
(SPC). These latter three methods presented good results when
processed using PC1 from ALOS/PALSAR-1 FBD backscatter filtered
image or three SAR extracted and selected features. These results
corroborate with the applicability of the proposed method for SAR
data information integration. Distinct methods better discriminate
different LULC classes. In general, densely forested classes were
better characterized by the Ehlers_TM6 fusion method, in which at
least the polarization HV was used. Intermediate and initial
regeneration classes were better discriminated using SPC-fused
data with PC1 of ALOS/PALSAR1 FBD data. Bare soil and pasture
classes were better discriminated in optical features and the PC1
of ALOS/PALSAR-1 FBD data fused by the IHS method. Soybean with
approximately 40 days from seeding was better discriminated in
image classification obtained from ALOS/PALSAR-1 FBD image.",
doi = "10.1109/JSTARS.2018.2853647",
url = "http://dx.doi.org/10.1109/JSTARS.2018.2853647",
issn = "1939-1404 and 2151-1535",
language = "en",
targetfile = "pereira_evaluation.pdf",
urlaccessdate = "27 abr. 2024"
}