scholarly journals Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA

2015 ◽  
Vol 7 (5) ◽  
pp. 5347-5369 ◽  
Author(s):  
Pengyu Hao ◽  
Yulin Zhan ◽  
Li Wang ◽  
Zheng Niu ◽  
Muhammad Shakir
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Random Forest ◽  
Modis Data ◽  
Early Crop ◽  
Crop Classification ◽  
Selection Of

scholarly journals Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China

2019 ◽  
Vol 11 (22) ◽  
pp. 2673 ◽  
Author(s):  
Hongwei Zhao ◽  
Zhongxin Chen ◽  
Hao Jiang ◽  
Wenlong Jing ◽  
Liang Sun ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Optical Remote Sensing ◽  
Learning Models ◽  
Learning Methods ◽  
Early Crop ◽  
Crop Classification ◽  
Incremental Classification ◽  
Time Point

Timely and accurate estimation of the area and distribution of crops is vital for food security. Optical remote sensing has been a key technique for acquiring crop area and conditions on regional to global scales, but great challenges arise due to frequent cloudy days in southern China. This makes optical remote sensing images usually unavailable. Synthetic aperture radar (SAR) could bridge this gap since it is less affected by clouds. The recent availability of Sentinel-1A (S1A) SAR imagery with a 12-day revisit period at a high spatial resolution of about 10 m makes it possible to fully utilize phenological information to improve early crop classification. In deep learning methods, one-dimensional convolutional neural networks (1D CNNs), long short-term memory recurrent neural networks (LSTM RNNs), and gated recurrent unit RNNs (GRU RNNs) have been shown to efficiently extract temporal features for classification tasks. However, due to the complexity of training, these three deep learning methods have been less used in early crop classification. In this work, we attempted to combine them with an incremental classification method to avoid the need for training optimal architectures and hyper-parameters for data from each time series. First, we trained 1D CNNs, LSTM RNNs, and GRU RNNs based on the full images’ time series to attain three classifiers with optimal architectures and hyper-parameters. Then, starting at the first time point, we performed an incremental classification process to train each classifier using all of the previous data, and obtained a classification network with all parameter values (including the hyper-parameters) at each time point. Finally, test accuracies of each time point were assessed for each crop type to determine the optimal time series length. A case study was conducted in Suixi and Leizhou counties of Zhanjiang City, China. To verify the effectiveness of this method, we also implemented the classic random forest (RF) approach. The results were as follows: (i) 1D CNNs achieved the highest Kappa coefficient (0.942) of the four classifiers, and the highest value (0.934) in the GRU RNNs time series was attained earlier than with other classifiers; (ii) all three deep learning methods and the RF achieved F measures above 0.900 before the end of growth seasons of banana, eucalyptus, second-season paddy rice, and sugarcane; while, the 1D CNN classifier was the only one that could obtain an F-measure above 0.900 for pineapple before harvest. All results indicated the effectiveness of the solution combining the deep learning models with the incremental classification approach for early crop classification. This method is expected to provide new perspectives for early mapping of croplands in cloudy areas.

Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study

2020 ◽  
Vol 93 ◽  
pp. 106389 ◽  
Author(s):  
Dongxiao Niu ◽  
Keke Wang ◽  
Lijie Sun ◽  
Jing Wu ◽  
Xiaomin Xu
Keyword(s):  
Power Generation ◽  
Feature Selection ◽  
Random Forest ◽  
Short Term ◽  
Photovoltaic Power

Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin

2019 ◽  
Vol 171 ◽  
pp. 36-50 ◽  
Author(s):  
Laura Piedelobo ◽  
David Hernández-López ◽  
Rocío Ballesteros ◽  
Amal Chakhar ◽  
Susana Del Pozo ◽  
...  
Keyword(s):  
Time Series ◽  
River Basin ◽  
Landsat 8 ◽  
Crop Classification ◽  
Sentinel 2

Feature Selection for Time Series Forecasting: A Case Study

2008 ◽  
Author(s):  
Rubén García Pajares ◽  
Jose M. Benítez ◽  
Gregorio Sáinz Palmero
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Time Series Forecasting ◽  
Selection For

scholarly journals Time Series Modeling of Air Pollution and Its Association with Season and Climate Variables in Istanbul, Turkey

2020 ◽  
Author(s):  
hazem al-najjar ◽  
Nadia Al-Rousan ◽  
Ismail A. Elhaty
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Feature Selection ◽  
Wind Speed ◽  
Air Pollutants ◽  
Time Series Modeling ◽  
Climate Variables ◽  
Different Seasons ◽  
The Impact

Abstract Air pollution depends on seasons, wind speed, temperature, wind direction and air pressure. The effect of different seasons on air pollution is not fully addressed in the reported works. The current study investigated the impact of season on air pollutants including SO2, PM10, NO, NOX, and O3 using NARX method. In the applied methodology, a feature selection was used with each pollutant to find the most important season(s). Afterward, six models are designed based on the feature selection to show the impact of seasons in finding the concentration of pollutants. A case study is conducted on Esenyurt which is one of the most populated and industrialized places in Istanbul to validate the proposed framework. The performance of using all of the designed models with different pollutants showed that using season effect led to improving the performance of predictor and generating high R2 and low error functions.

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