Predicting subsurface thermohaline structure from remote sensing data based on long short-term memory neural networks

2021 ◽  
Vol 260 ◽  
pp. 112465
Author(s):  
Hua Su ◽  
Tianyi Zhang ◽  
Mengjing Lin ◽  
Wenfang Lu ◽  
Xiao-Hai Yan
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Short Term Memory ◽  
Remote Sensing Data ◽  
Short Term ◽  
Term Memory ◽  
Thermohaline Structure ◽  
Sensing Data ◽  
Long Short Term Memory

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.

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