scholarly journals A Hybrid Model of Bidirectional Long-Short Term Memory and CNN for Multivariate Time Series Classification of Remote Sensing Data

2021 ◽  
Vol 17 (9) ◽  
pp. 789-802
Author(s):  
Sawsan Morkos Gharghory
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Remote Sensing Data ◽  
Time Series Classification ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Time Series Forecasting and Classification Models Based on Recurrent with Attention Mechanism and Generative Adversarial Networks

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7211
Author(s):  
Kun Zhou ◽  
Wenyong Wang ◽  
Teng Hu ◽  
Kai Deng
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Time Series Classification ◽  
Training Process ◽  
Short Term ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Term Memory ◽  
Adversarial Network ◽  
Long Short Term Memory

Time series classification and forecasting have long been studied with the traditional statistical methods. Recently, deep learning achieved remarkable successes in areas such as image, text, video, audio processing, etc. However, research studies conducted with deep neural networks in these fields are not abundant. Therefore, in this paper, we aim to propose and evaluate several state-of-the-art neural network models in these fields. We first review the basics of representative models, namely long short-term memory and its variants, the temporal convolutional network and the generative adversarial network. Then, long short-term memory with autoencoder and attention-based models, the temporal convolutional network and the generative adversarial model are proposed and applied to time series classification and forecasting. Gaussian sliding window weights are proposed to speed the training process up. Finally, the performances of the proposed methods are assessed using five optimizers and loss functions with the public benchmark datasets, and comparisons between the proposed temporal convolutional network and several classical models are conducted. Experiments show the proposed models’ effectiveness and confirm that the temporal convolutional network is superior to long short-term memory models in sequence modeling. We conclude that the proposed temporal convolutional network reduces time consumption to around 80% compared to others while retaining the same accuracy. The unstable training process for generative adversarial network is circumvented by tuning hyperparameters and carefully choosing the appropriate optimizer of “Adam”. The proposed generative adversarial network also achieves comparable forecasting accuracy with traditional methods.

scholarly journals Semi-Supervised Convolutional Long Short-Term Memory Neural Networks for Time Series Land Cover Classification

2021 ◽  
Vol 13 (17) ◽  
pp. 3504
Author(s):  
Jing Shen ◽  
Chao Tao ◽  
Ji Qi ◽  
Hao Wang
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Time Series ◽  
Land Cover ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Remote Sensing Images ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Time series images with temporal features are beneficial to improve the classification accuracy. For abstract temporal and spatial contextual information, deep neural networks have become an effective method. However, there is usually a lack of sufficient samples in network training: one is the loss of images or the discontinuous distribution of time series data because of the inevitable cloud cover, and the other is the lack of known labeled data. In this paper, we proposed a Semi-supervised convolutional Long Short-Term Memory neural network (SemiLSTM) for time series remote sensing images, which was validated on three data sets with different time distributions. It achieves an accurate and automated land cover classification via a small number of labeled samples and a large number of unlabeled samples. Besides, it is a robust classification algorithm for time series optical images with cloud coverage, which reduces the requirements for cloudless remote sensing images and can be widely used in areas that are often obscured by clouds, such as subtropical areas. In conclusion, this method makes full advantage of spectral-spatial-temporal characteristics under the condition of limited training samples, especially expanding time context information to enhance classification accuracy.

Prediction of InSAR deformation time-series using a long short-term memory neural network

2021 ◽  
Vol 42 (18) ◽  
pp. 6921-6944
Author(s):  
Yi Chen ◽  
Yi He ◽  
Lifeng Zhang ◽  
Youdong Chen ◽  
Hongyu Pu ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory
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