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Title: Rice crop detection using LSTM, Bi-LSTM, and machine learning models from sentinel-1 time series
Authors: Castro Filho, Hugo Crisóstomo de
Carvalho Júnior, Osmar Abílio de
Carvalho, Osmar Luiz Ferreira de
Bem, Pablo Pozzobon de
Moura, Rebeca dos Santos de
Albuquerque, Anesmar Olino de
Silva, Cristiano Rosa
Ferreira, Pedro Henrique Guimarães
Guimarães, Renato Fontes
Gomes, Roberto Arnaldo Trancoso
metadata.dc.identifier.orcid: 0000-0002-0346-1684 0000-0002-5619-8525 0000-0003-3868-8704 0000-0002-7685-8826 0000-0003-1561-7583 0000-0001-6610-3078 0000-0002-9555-043X 0000-0003-4724-4064
Assunto:: Monitoramento de safras
Imagem multitemporal
Aprendizado profundo
Aprendizado de máquina
Rede neural recorrente
Issue Date: 18-Aug-2020
Publisher: MDPI
Citation: CASTRO FILHO, Hugo Crisóstomo de et al. Rice crop detection using LSTM, Bi-LSTM, and machine learning models from sentinel-1 time series. Remote Sensing, v. 12, n. 16, 2655, 2020. DOI: Disponível em: Acesso em: 25 ago. 2021.
Abstract: The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul.
Licença:: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Appears in Collections:ENE - Artigos publicados em periódicos
GEA - Artigos publicados em periódicos

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