# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 5 Conclusions and Discussion
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] Learning spatial-temporal features for video copy detection by the combination of CNN and RNN. _J. Vis. Commun. Image Represent._**2018**, _55_, 21-29. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Spatial-Temporal Recurrent Neural Network for Emotion Recognition. _IEEE Trans. Cybern._**2018**, 839-847. [CrossRef] [PubMed]
* [PERSON] et al. (2019) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network. _Remote Sens._**2019**, _11_, 1157. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] [PERSON] Text Normalization Using Encoder-Decoder Networks Based on the Causal Feature Extractor. _Appl. Sci._**2020**, _10_, 4551. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder-Decoder Network. _Appl. Sci._**2020**, _10_, 6621. [CrossRef]
* [PERSON] and [PERSON] (2014) [PERSON]; [PERSON]; [PERSON] Sequence to sequence learning with neural networks. In _Advances in Neural Information Processing Systems_; Neural Information Processing Systems Foundation, Inc.: South Lake Tahoe, NV, USA, 2014; pp. 3104-3112. * [PERSON] et al. (2015) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Long-term recurrent convolutional networks for visual recognition and description. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7-12 June 2015; pp. 2625-2634. * [PERSON] (1997) [PERSON]; [PERSON] [PERSON] Long Short-term Memory. _Neural Comput._**1997**, \(9\), 1735-1780. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] [PERSON] Unsupervised learning of video representations using LSTMs. In Proceedings of the 32 nd International Conference on International Conference on Machine Learning, Lille, France, 7-9 July 2015; Volume 37, pp. 843-852. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep learning for precipitation nowcasting: A benchmark and a new model. In Proceedings of the 31 st International Conference on Neural Information Processing Systems, Long Beach, CA, USA, 4-9 December 2017; pp. 5622-5632. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] PredRNN++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning. _Proc. Mach. Learn. Res._**2018**, _80_, 5123-5132. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs. In _Advances in Neural Information Processing Systems 30_; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]; Curran Associates, Inc.: New York, NY, USA, 2017; pp. 879-888. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 16-20 June 2019; pp. 9154-9162. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Eidetic 3D LSTM: A Model for Video Prediction and Beyond. In Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA, 6-9 May 2019. * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] U-Net Convolutional Networks for Biomedical Image Segmentation. _Exp. Algorithms_**2015**, _9351_, 234-241. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++. _Remote Sens._**2019**, _11_, 1382. [CrossRef]
* [PERSON] et al.