| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Meio Ambiente. |
|
Data corrente: |
02/01/2019 |
|
Data da última atualização: |
17/01/2023 |
|
Tipo da produção científica: |
Artigo em Anais de Congresso |
|
Autoria: |
SANCHES, I. D.; FEITOSA, R. Q.; ACHANCCARAY, P.; MONTIBELLER, B.; LUIZ, A. J. B.; SOARES, M. D.; PRUDENTE, V. H. R.; VIEIRA, D. C.; MAURANO, L. E. P. |
|
Afiliação: |
IEDA DEL'ARCO SANCHES, INPE; RAUL QUEIROZ FEITOSA, PUC Rio; PEDRO ACHANCCARAY, PUC Rio; BRUNO MONTIBELLER, INPE; ALFREDO JOSE BARRETO LUIZ, CNPMA; MARINALVA DIAS SOARES, PUC Rio; VICTOR HUGO ROHDEN PRUDENTE, INPE; D C VIEIRA, INPE; LUIS EDUARDO PINHEIRO MAURANO, INPE. |
|
Título: |
Lem benchmark database for tropical agricultural remote sensing application. |
|
Ano de publicação: |
2018 |
|
Fonte/Imprenta: |
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42, n. 1, p. 387-392, 2018. Edition of the proceedings ISPRS TC I Mid-term Symposium ?Innovative Sensing ? From Sensors to Methods and Applications?, 10-12 October 2018, held a Karlsruhe, Germany. |
|
Páginas: |
387-392. |
|
Idioma: |
Inglês |
|
Conteúdo: |
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 topic?s 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. MenosAbstract: 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 topic?s 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 t... Mostrar Tudo |
|
Palavras-Chave: |
Agricultura tropical; Agricultural mapping/monitoring; C-band SAR data; Double gropping systems; Free available database; Mapeamento; Multispectral instrument. |
|
Thesagro: |
Agricultura; Base de dados; Sensoriamento remoto. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/189604/1/2018AA07.pdf
|
|
Marc: |
LEADER 02926nam a2200337 a 4500
001 2102815
005 2023-01-17
008 2018 bl uuuu u00u1 u #d
100 1 $aSANCHES, I. D.
245 $aLem benchmark database for tropical agricultural remote sensing application.$h[electronic resource]
260 $aInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42, n. 1, p. 387-392, 2018. Edition of the proceedings ISPRS TC I Mid-term Symposium ?Innovative Sensing ? From Sensors to Methods and Applications?, 10-12 October 2018, held a Karlsruhe, Germany.$c2018
300 $a387-392.
520 $aAbstract: 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 topic?s 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.
650 $aAgricultura
650 $aBase de dados
650 $aSensoriamento remoto
653 $aAgricultura tropical
653 $aAgricultural mapping/monitoring
653 $aC-band SAR data
653 $aDouble gropping systems
653 $aFree available database
653 $aMapeamento
653 $aMultispectral instrument
700 1 $aFEITOSA, R. Q.
700 1 $aACHANCCARAY, P.
700 1 $aMONTIBELLER, B.
700 1 $aLUIZ, A. J. B.
700 1 $aSOARES, M. D.
700 1 $aPRUDENTE, V. H. R.
700 1 $aVIEIRA, D. C.
700 1 $aMAURANO, L. E. P.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Meio Ambiente (CNPMA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
| Registros recuperados : 1 | |
| 1. |  | SANCHES, I. D.; FEITOSA, R. Q.; ACHANCCARAY, P.; MONTIBELLER, B.; LUIZ, A. J. B.; SOARES, M. D.; PRUDENTE, V. H. R.; VIEIRA, D. C.; MAURANO, L. E. P. Lem benchmark database for tropical agricultural remote sensing application. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42, n. 1, p. 387-392, 2018. Edition of the proceedings ISPRS TC I Mid-term Symposium ?Innovative Sensing ? From Sensors to Methods and Applications?, 10-12 October 2018, held a Karlsruhe, Germany. 387-392.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Meio Ambiente. |
|    |
| Registros recuperados : 1 | |
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
