Using An Attention-Based LSTM Encoder-Decoder Network for Near Real-Time Disturbance Detection

Accurate prediction of future observations based on past data is the key to near real-time disturbance detection using satellite image time series (SITS). To overcome the limitations of existing methods, we present an attention-based long-short-term memory (LSTM) encoder-decoder model in which the h...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE journal of selected topics in applied earth observations and remote sensing Vol. 13; pp. 1819 - 1832
Main Authors:	Yuan, Yuan, Lin, Lei, Huo, Lian-Zhi, Kong, Yun-Long, Zhou, Zeng-Guang, Wu, Bin, Jia, Yan
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Attention mechanism Coders Detection Early warning systems encoder–decoder False alarms Forecasting Hidden Markov models Image detection Long short-term memory long-short-term memory (LSTM) Mapping Methods Model accuracy near real-time disturbance detection Predictions Predictive models Real time Real-time systems Remote sensing satellite image time series (SITS) Satellite imagery Satellites Spaceborne remote sensing Spectroradiometers Time series Time series analysis Warning systems
ISSN:	1939-1404, 2151-1535
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!