Abstract
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.
Highlights
In recent years, the analysis of long time-series remote sensing data has attracted extensive attention from researchers who use multitemporal remote sensing images to construct time series and perform change detection using time series analysis methods [1]
We propose an long short-term memory (LSTM)-based framework for online disturbance detection in satellite image time series (SITS)
We present the framework for online disturbance detection in SITS based on LSTM in detail
Summary
The analysis of long time-series remote sensing data has attracted extensive attention from researchers who use multitemporal remote sensing images to construct time series and perform change detection using time series analysis methods [1]. In the field of remote sensing, researchers worldwide have employed satellite image time series (SITS) in the dynamic monitoring of natural phenology, landscapes, disasters, agriculture, and other areas [7,8,9,10]. Based on their objectives, SITS change detection techniques can be grouped into two categories [11]: (1) methods oriented to detect land cover/use types change and (2) methods oriented to detect anomalous information due to sudden and unexpected events. Online detection is advantageous because, in many cases, detection of anomalies must be done in a timely manner so that counter-measures can be taken promptly (for example, responses to wildfire, flood, etc.)
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