@Article{SanchesFAMLSPVM:2018:LEBeDa,
author = "Sanches, Ieda Del'Arco and Feitosa, Raul Q. and Achanccaray, P.
and Montibeller, Bruno and Luiz, Alfredo J. B. and Soares, M. Dias
and Prudente, Victor Hugo Rohden and Vieira, Denis Corte and
Maurano, Luis Eduardo Pinheiro",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Pontif{\'{\i}}cia Universidade do Rio de Janeiro (PUC-Rio)} and
{Pontif{\'{\i}}cia Universidade do Rio de Janeiro (PUC-Rio)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Empresa
Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and
{Pontif{\'{\i}}cia Universidade do Rio de Janeiro (PUC-Rio)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "LEM benchmark database for tropical agricultural remote sensing
application",
journal = "International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences",
year = "2018",
volume = "42",
number = "1",
pages = "387--392",
month = "Sept.",
keywords = "Free available database, MultiSpectral Instrument, C-Band SAR
data, Agricultural Mapping/Monitoring, Double Cropping Systems.",
abstract = "The monitoring of agricultural activities at a regular basis is
crucial to assure that the food production meets the world
population demands, which is increasing yearly. Such information
can be derived from remote sensing data. In spite of topics
relevance, not enough efforts have been invested to exploit modern
pattern recognition and machine learning methods for agricultural
land-cover mapping from multi-temporal, multi-sensor earth
observation data. Furthermore, only a small proportion of the
works published on this topic relates to tropical/subtropical
regions, where crop dynamics is more complicated and difficult to
model than in temperate regions. A major hindrance has been the
lack of accurate public databases for the comparison of different
classification methods. In this context, the aim of the present
paper is to share a multi-temporal and multi-sensor benchmark
database that can be used by the remote sensing community for
agricultural land-cover mapping. Information about crops in situ
was collected in Lu{\'{\i}}s Eduardo Magalh{\~a}es (LEM)
municipality, which is an important Brazilian agricultural area,
to create field reference data including information about first
and second crop harvests. Moreover, a series of remote sensing
images was acquired and pre-processed, from both active and
passive orbital sensors (Sentinel-1, Sentinel-2/MSI,
Landsat-8/OLI), correspondent to the LEM area, along the
development of the main annual crops. In this paper, we describe
the LEM database (crop field boundaries, land use reference data
and pre-processed images) and present the results of an experiment
conducted using the Sentinel-1 and Sentinel-2 data.",
doi = "10.5194/isprs-archives-XLII-1-387-2018",
url = "http://dx.doi.org/10.5194/isprs-archives-XLII-1-387-2018",
issn = "0256-1840",
language = "en",
targetfile = "sanches_lem.pdf",
urlaccessdate = "27 abr. 2024"
}