Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series
| Autor: | Osmar Abílio de Carvalho Júnior, Roberto Arnaldo Trancoso Gomes, Osmar Luiz Ferreira de Carvalho, Renato Fontes Guimarães, Pedro Henrique Guimarães Ferreira, Pablo Pozzobon de Bem, Rebeca dos Santos de Moura, Anesmar Olino de Albuquerque, Cristiano Rosa Silva, Hugo Crisóstomo de Castro Filho |
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| Rok vydání: | 2020 |
| Předmět: |
Synthetic aperture radar
010504 meteorology & atmospheric sciences Computer science 0211 other engineering and technologies 02 engineering and technology Machine learning computer.software_genre 01 natural sciences law.invention law Radar lcsh:Science 021101 geological & geomatics engineering 0105 earth and related environmental sciences monitoring crops business.industry Deep learning deep learning Aprendizado de máquina Random forest Support vector machine multitemporal image machine learning recurrent neural network Recurrent neural network Pattern recognition (psychology) General Earth and Planetary Sciences lcsh:Q Rede neural recorrente Artificial intelligence Aprendizado profundo business computer Smoothing Monitoramento de safras Imagem multitemporal |
| Zdroj: | Repositório Institucional da UnB Universidade de Brasília (UnB) instacron:UNB Remote Sensing, Vol 12, Iss 2655, p 2655 (2020) Remote Sensing; Volume 12; Issue 16; Pages: 2655 |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs12162655 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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