Data/hora: 16/06/2024 02:55:21

Biblioteca(s): | Embrapa Agricultura Digital.
Tipo da produção científica: | Artigo em Periódico Indexado
Circulação/Nível: | A - 1
Data corrente: | 20/12/2017
Data da última atualização: | 12/02/2020
Autoria: | TOMÀS, J. C.; FARIA, F. A.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; MEDEIROS, C. B.
Afiliação: | JORDI CREUS TOMÀS, IC/Unicamp; FABIO AUGUSTO FARIA, Federal University of São Paulo, São José dos Campos; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; CLAUDIA BAUZER MEDEIROS, IC/Unicamp.
Título: | SiRCub: a novel approach to recognize agricultural crops using supervised classification.
Ano de publicação: | 2017
Fonte/Imprenta: | International Journal of Agricultural and Environmental Information Systems, v. 8, n. 4, p. 20-36, Oct./Dec. 2017.
DOI: | 10.4018/IJAEIS.2017100102
Idioma: | Inglês
Notas: | Publicado também em: INFORMATION RESOURCES MANAGEMENT ASSOCIATION (Ed.). Environmental information systems: concepts, methodologies, tools, and applications. Hershey: IGI Global, 2019. v. II, chap. 50, p. 1129-1147. DOI: 10.4018/978-1-5225-7033-2.ch050.
Conteúdo: | This paper presents a new approach to deal with agricultural crop recognition using SVM (Support Vector Machine), applied to time series of NDVI images. The presented method can be divided into two steps. First, the Timesat software package is used to extract a set of crop features from the NDVI time series. These features serve as descriptors that characterize each NDVI vegetation curve, i.e., the period comprised between sowing and harvesting dates. Then, it is used an SVM to learn the patterns that define each type of crop, and create a crop model that allows classifying new series. The authors present a set of experiments that show the effectiveness of this technique. They evaluated their algorithm with a collection of more than 3000 time series from the Brazilian State of Mato Grosso spanning 4 years (2009-2013). Such time series were annotated in the field by specialists from Embrapa (Brazilian Agricultural Research Corporation). This methodology is generic, and can be adapted to distinct regions and crop profiles.
Palavras-Chave: | Aprendizado de máquina; Crop classification; Enhanced Vegetation Index; Índice de vegetação; LULC; Machine Learning; Séries temporais; SIRCub; Support Vector Machine.
Thesagro: | Sensoriamento remoto; Uso da terra.
Thesaurus Nal: | Artificial intelligence; Land cover; Land use; normalized difference vegetation index; Remote sensing; Time series analysis; Vegetation index.
Categoria do assunto: | X Pesquisa, Tecnologia e Engenharia
Registro original: | Embrapa Agricultura Digital (CNPTIA)
Exemplares: | CNPTIA - (19538-1) - UPC - AP - DD -  -  -  - DIS