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

文献类型：期刊论文

作者Shen, Jing1,2; Tao, Chao1; Qi, Ji1; Wang, Hao1
刊名REMOTE SENSING
出版日期2021-09-01
卷号13期号:17页码:18
关键词image classification Long Short-Term Memory neural network (LSTM) remote sensing semi-supervised learning time series analysis
DOI10.3390/rs13173504
通讯作者Tao, Chao(kingtaochao@126.com)
英文摘要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.
WOS关键词IMAGERY ; DATABASE
资助项目National Natural Science Foundation of China[42171376] ; National Natural Science Foundation of China[41771458] ; National Natural Science Foundation of China[41871364] ; Young Elite Scientists Sponsorship Program by Hunan province of China[2018RS3012] ; Hunan Science and Technology Department Innovation Platform Open Fund Project[18K005] ; Postgraduate Scientific Research Innovation Project of Hunan Province[CX20200325] ; Fundamental Research Funds for the Central Universities of Central South University[2020zzts671]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000694480700001
出版者MDPI
资助机构National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program by Hunan province of China ; Hunan Science and Technology Department Innovation Platform Open Fund Project ; Postgraduate Scientific Research Innovation Project of Hunan Province ; Fundamental Research Funds for the Central Universities of Central South University
源URL[http://ir.igsnrr.ac.cn/handle/311030/165519]  
专题中国科学院地理科学与资源研究所
通讯作者Tao, Chao
作者单位1.Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Shen, Jing,Tao, Chao,Qi, Ji,et al. Semi-Supervised Convolutional Long Short-Term Memory Neural Networks for Time Series Land Cover Classification[J]. REMOTE SENSING,2021,13(17):18.
APA Shen, Jing,Tao, Chao,Qi, Ji,&Wang, Hao.(2021).Semi-Supervised Convolutional Long Short-Term Memory Neural Networks for Time Series Land Cover Classification.REMOTE SENSING,13(17),18.
MLA Shen, Jing,et al."Semi-Supervised Convolutional Long Short-Term Memory Neural Networks for Time Series Land Cover Classification".REMOTE SENSING 13.17(2021):18.

入库方式： OAI收割

来源：地理科学与资源研究所

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