Channel Attention-Based Temporal Convolutional Network for Satellite Image Time Series Classification

Tang, Pengfei; Du, Peijun; Xia, Junshi; Zhang, Peng; Zhang, Wei

doi:10.1109/lgrs.2021.3095505

Cited by 30 publications

(19 citation statements)

References 20 publications

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“…The TCN [ 43 ] model is a variation of the CNN and is commonly used for sequence modeling tasks. The TCN working is to encode information from an input sequence.…”

Section: Methodsmentioning

confidence: 99%

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Raza

Siddiqui²,

Munir³

et al. 2022

PLoS ONE

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Maternal health is an important aspect of women’s health during pregnancy, childbirth, and the postpartum period. Specifically, during pregnancy, different health factors like age, blood disorders, heart rate, etc. can lead to pregnancy complications. Detecting such health factors can alleviate the risk of pregnancy-related complications. This study aims to develop an artificial neural network-based system for predicting maternal health risks using health data records. A novel deep neural network architecture, DT-BiLTCN is proposed that uses decision trees, a bidirectional long short-term memory network, and a temporal convolutional network. Experiments involve using a dataset of 1218 samples collected from maternal health care, hospitals, and community clinics using the IoT-based risk monitoring system. Class imbalance is resolved using the synthetic minority oversampling technique. DT-BiLTCN provides a feature set to obtain high accuracy results which in this case are provided by the support vector machine with a 98% accuracy. Maternal health exploratory data analysis reveals that the health conditions which are the strongest indications of health risk during pregnancy are diastolic and systolic blood pressure, heart rate, and age of pregnant women. Using the proposed model, timely prediction of health risks associated with pregnant women can be made thus mitigating the risk of health complications which helps to save lives.

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“…The TCN [ 43 ] model is a variation of the CNN and is commonly used for sequence modeling tasks. The TCN working is to encode information from an input sequence.…”

Section: Methodsmentioning

confidence: 99%

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Raza

Siddiqui²,

Munir³

et al. 2022

PLoS ONE

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show abstract

“…For the next study, the development of deep learning using remote sensing time-series dataset can be tested for mapping crop intensity. Deep neural network architectures, such as one-dimensional Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Temporal Convolutional Network (TCN), have been used for obtaining phenological information and crop classification [50,51]. Although, a study by Rußwurm and Körner [52] suggested that conventional machine learning algorithms such as RF can give competitive accuracy for classification as long as proper pre-processing (atmospheric correction and data-selection) to the satellite imagery is conducted.…”

Section: Discussionmentioning

confidence: 99%

Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data

Rafif

Kusuma

Saringatin

et al. 2021

Land

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Crop intensity information describes the productivity and the sustainability of agricultural land. This information can be used to determine which agricultural lands should be prioritized for intensification or protection. Time-series data from remote sensing can be used to derive the crop intensity information; however, this application is limited when using medium to coarse resolution data. This study aims to use 3.7 m-PlanetScope™ Dove constellation data, which provides daily observations, to map crop intensity information for agricultural land in Magelang District, Indonesia. Two-stage histogram matching, before and after the monthly median composites, is used to normalize the PlanetScope data and to generate monthly data to map crop intensity information. Several methods including Time-Weighted Dynamic Time Warping (TWDTW) and the machine-learning algorithms: Random Forest (RF), Extremely Randomized Trees (ET), and Extreme Gradient Boosting (XGB) are employed in this study, and the results are validated using field survey data. Our results show that XGB generated the highest overall accuracy (OA) (95 ± 4%), followed by RF (92 ± 5%), ET (87 ± 6%), and TWDTW (81 ± 8%), for mapping four-classes of cropping intensity, with the near-infrared (NIR) band being the most important variable for identifying cropping intensity. This study demonstrates the potential of PlanetScope data for the production of cropping intensity maps at detailed resolutions.

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“…Therefore, it may be a more suitable starting point for applying deep networks to sequence processing.Hewage P et al developed a lightweight prediction system using TCN for modeling time series [26].P. Tang et al proposed a temporal convolutional model based on channel attention, which achieved satellite image time series classification with fewer parameters [27].…”

Section: Related Workmentioning

confidence: 99%

Long-Term Prediction of Network Security Situation Through the Use of the Transformer-Based Model

Yin¹,

Yang²,

Yao³

et al. 2022

IEEE Access

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In recent years, the Internet has shown rapid development, and network security issue has gradually become the focus of research by scholars and enterprises. Network security time series is a reliable source to obtain future network security situation, so as to develop network security defense strategy by exploring the correlation of time series. The network security time series is a reliable source to obtain the future network security situation, and it is the main direction of current network security defense by exploring the correlation of time series, and analyzing the future network security situation so as to formulate network security defense strategies. This is the main direction of network security defense. The existing research focuses on the short-term prediction of network attacks, and the robustness and accuracy of long-term prediction still have big problems. To fuse the information from different data sources and capture the correlation between sequences, we design a data source selection module based on the similarity of measurement curves. We then model the network security situation prediction based on deep learning models and propose a situation prediction model based on Temporal Convolutional Network (TCN)combined Transformer, which focuses on the time series long-term prediction problem, combining the network condition and attack situation to obtain the future network security situation. Our proposed model is divided into three parts, which are the information encoding module, the information synthesis module, and situation value calculation and prediction accuracy evaluation module. The selected multi-dimensional situations element data are used as model input, and the TCN-combined Transformer is employed as the network security situational data processing unit to complete the information fusion and prediction tasks. Finally, the role of data source selection on prediction accuracy is evaluated using an ablation study. We experimented and evaluated the model at different prediction horizon lengths using five existing baseline models and three performance metrics. The experimental results show that our proposed prediction model has better robustness and accuracy in most of the metrics.

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Channel Attention-Based Temporal Convolutional Network for Satellite Image Time Series Classification

Cited by 30 publications

References 20 publications

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data

Long-Term Prediction of Network Security Situation Through the Use of the Transformer-Based Model

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