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

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...

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Bibliographic Details
Main Authors: Yuan Yuan, Lei Lin, Lian-Zhi Huo, Yun-Long Kong, Zeng-Guang Zhou, Bin Wu, Yan Jia
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Attention mechanism
encoder–decoder
long-short-term memory (LSTM)
near real-time disturbance detection
satellite image time series (SITS)
Online Access:https://ieeexplore.ieee.org/document/9072638/

Internet

https://ieeexplore.ieee.org/document/9072638/

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