@Article{OldoniPrDiWiSaGa:2020:PoSADa,
author = "Oldoni, Lucas Volochen and Prudente, Victor Hugo Rohden and Diniz,
Juliana Maria Ferreira de Souza and Wiederkehr, Nat{\'a}lia
Cristina and Sanches, Ieda Del'Arco and Gama, F{\'a}bio Furlan",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Polarimetric SAR data from sentinel-1a applied to early crop
classification",
journal = "International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences",
year = "2020",
volume = "43",
number = "B3",
pages = "1039--1046",
month = "Aug.",
note = "2020 24th ISPRS Congress - Technical Commission III; Nice,
Virtual; France; 31 August 2020 through 2 September 2020",
keywords = "Agriculture monitoring, Remote Sensing, Microwave, Soybean, Early
classification, Machine learning.",
abstract = "This paper aims to map crops in two Brazilian municipalities,
Lu{\'{\i}}s Eduardo Magalh{\~a}es (LEM) and Campo Verde, using
dualpolarimetric Sentinel-1A images. The specific objectives were:
(1) to evaluate the accuracy gain in the crop classification using
Sentinel-1A multitemporal data backscatter coefficients and ratio
(\σ0VH, \σ0VV and, \σ0VH/\σ0VV, denominate
BS group) in comparison to the addition of polarimetric attributes
(\σ0VH, \σ0VV, \σ 0VH/\σ0VV, H, and
\α, denominate BP group) and; (2) to assess the accuracy
gain in the earliest crop classification, creating new scenarios
with the addition of the new SAR data together with the previous
images for each date and group (BS and BP) during the crop
development. For BS and BP groups, 13 e 10 scenarios were analyzed
in LEM and Campo Verde, respectively. For the classification
process, we used the Random Forest (RF) algorithm. In the LEM
site, the best results for BS and BP groups were equivalent
(overall accuracy: ~82%), while for the Campo Verde site, the
classification accuracy for the BP group (overall accuracy: ~80%)
was 2% higher than the BS group. The addition of new images during
the crop development period increased the earliest crop
classification overall accuracy, stabilizing from mid-February in
LEM and mid-December in Campo Verde, after 10 and 8 images,
respectively. After these periods, the gain in classification
accuracy was small with the addition of new images. In general,
our results suggest the backscattering coefficients and
polarimetric attributes extracted from the Sentinel-1A imagery
exhibited a great performance to discriminate croplands.",
doi = "10.5194/isprs-archives-XLIII-B3-2020-1039-2020",
url = "http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2020-1039-2020",
issn = "0256-1840",
language = "en",
targetfile = "oldoni_polarimetric.pdf",
urlaccessdate = "29 mar. 2024"
}