Data/hora: 19/06/2024 19:26:44

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	14/12/2021	
Data da última atualização: 	14/12/2021	
Autoria: 	ALMEIDA, H. S. L.; REIS, A. A. dos; WERNER, J. P. S.; ANTUNES, J. F. G.; ZHONG, L.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.	
Afiliação: 	HENRIQUE S. L. ALMEIDA, UNICAMP; ALINY APARECIDA DOS REIS, UNICAMP; JOÃO PAULO SAMPAIO WERNER, UNICAMP; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; LIHENG ZHONG, Ant Group, World Financial Center, Beijing; GLEYCE KELLY DANTAS ARAÚJO FIGUEIREDO, UNICAMP; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; RUBENS AUGUSTO CAMARGO LAMPARELLI, UNICAMP; PAULO S. G. MAGALHÃES, UNICAMP.	
Título: 	Deep neural networks for mapping integrated crop-livestock systems using PlanetScope time series.	
Ano de publicação: 	2021	
Fonte/Imprenta: 	IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2021, Brussels. Proceedings [...]. [S. l.]: IEEE, 2021.	
Páginas: 	p. 4224-4227.	
ISBN: 	978-1-6654-0369-6	
DOI: 	10.1109/IGARSS47720.2021.9554500	
Idioma: 	Inglês	
Notas: 	IGARSS 2021. Paper WE2.MM-8.3.	
Conteúdo: 	Abstract: Mapping highly dynamic cropping systems using satellite image time series is still challenging even when robust approaches are used. We assessed the potential of using high spatial and temporal resolution PlanetScope time series and deep neural networks (Convolutional Neural Networks (CNN) in one dimension - Conv1D, Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP)) for mapping integrated crop-livestock systems (ICLS) and different land covers in the western region of São Paulo State, Brazil. We used 10-day and 15-day composite EVI and NDVI time series (both individually and combined) as input data in the neural network classifiers. Conv1D using both EVI and NDVI 10 day-composite time series outperformed the other classifiers evaluated in this study (LSTM and MLP), allowing improved discrimination of land parcels with ICLS in our study area.	
Palavras-Chave: 	Aprendizado profundo; Convolutional Neural Networks; Deep learning; EVI; Nano-Satellites; Nanossatélites; NDVI; Redes neurais; Redes neurais convolucionais; Redes neurais profundas; Séries temporais; Sistemas de integração lavoura-pecuária.	
Thesaurus Nal: 	Neural networks; Time series analysis.	
Categoria do assunto: 	--	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (21107-1) - UPC - AA - DD -  -  -  - DIS