%0 Journal Article
%4 sid.inpe.br/mtc-m21c/2018/11.19.15.56
%2 sid.inpe.br/mtc-m21c/2018/11.19.15.56.42
%@doi 10.5194/isprs-archives-XLII-1-387-2018
%@issn 0256-1840
%T LEM benchmark database for tropical agricultural remote sensing application
%D 2018
%8 Sept.
%9 conference paper
%A Sanches, Ieda Del'Arco,
%A Feitosa, Raul Q.,
%A Achanccaray, P.,
%A Montibeller, Bruno,
%A Luiz, Alfredo J. B.,
%A Soares, M. Dias,
%A Prudente, Victor Hugo Rohden,
%A Vieira, Denis Corte,
%A Maurano, Luis Eduardo Pinheiro,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Pontifícia Universidade do Rio de Janeiro (PUC-Rio)
%@affiliation Pontifícia Universidade do Rio de Janeiro (PUC-Rio)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
%@affiliation Pontifícia Universidade do Rio de Janeiro (PUC-Rio)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress ieda.sanches@inpe.br
%@electronicmailaddress raul@ele.puc-rio.br
%@electronicmailaddress pmad9589@ele.puc-rio.br
%@electronicmailaddress brunomontibeller93@gmail.com
%@electronicmailaddress alfredo.luiz@embrapa.br
%@electronicmailaddress mdiasoares@gmail.com
%@electronicmailaddress victor.prudente@inpe.br
%@electronicmailaddress denis.vieira@inpe.br
%@electronicmailaddress luis.maurano@inpe.br
%B International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
%V 42
%N 1
%P 387-392
%K Free available database, MultiSpectral Instrument, C-Band SAR data, Agricultural Mapping/Monitoring, Double Cropping Systems.
%X 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 Luís Eduardo Magalhães (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.
%@language en
%3 sanches_lem.pdf