Next Article in Journal
The Effect of Surface Waves on Airborne Lidar Bathymetry (ALB) Measurement Uncertainties
Previous Article in Journal
Object-Based Features for House Detection from RGB High-Resolution Images
Previous Article in Special Issue
Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks
Open AccessTechnical Note

Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 452; https://doi.org/10.3390/rs10030452
Received: 27 December 2017 / Revised: 3 March 2018 / Accepted: 12 March 2018 / Published: 13 March 2018
A satellite image time series (SITS) contains a significant amount of temporal information. By analysing this type of data, the pattern of the changes in the object of concern can be explored. The natural change in the Earth’s surface is relatively slow and exhibits a pronounced pattern. Some natural events (for example, fires, floods, plant diseases, and insect pests) and human activities (for example, deforestation and urbanisation) will disturb this pattern and cause a relatively profound change on the Earth’s surface. These events are usually referred to as disturbances. However, disturbances in ecosystems are not easy to detect from SITS data, because SITS contain combined information on disturbances, phenological variations and noise in remote sensing data. In this paper, a novel framework is proposed for online disturbance detection from SITS. The framework is based on long short-term memory (LSTM) networks. First, LSTM networks are trained by historical SITS. The trained LSTM networks are then used to predict new time series data. Last, the predicted data are compared with real data, and the noticeable deviations reveal disturbances. Experimental results using 16-day compositions of the moderate resolution imaging spectroradiometer (MOD13Q1) illustrate the effectiveness and stability of the proposed approach for online disturbance detection. View Full-Text
Keywords: long short-term memory; LSTM; recurrent neural network; RNN; online disturbance detection; satellite image time series; SITS long short-term memory; LSTM; recurrent neural network; RNN; online disturbance detection; satellite image time series; SITS
Show Figures

Graphical abstract

MDPI and ACS Style

Kong, Y.-L.; Huang, Q.; Wang, C.; Chen, J.; Chen, J.; He, D. Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series. Remote Sens. 2018, 10, 452.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop