Abstract
Technological evolution in the remote sensing domain has allowed the acquisition of large archives of satellite image time series (SITS) for Earth Observation. In this context, the need to interpret Earth Observation image time series is continuously increasing and the extraction of information from these archives has become difficult without adequate tools. In this paper, we propose a fast and effective two-step technique for the retrieval of spatio-temporal patterns that are similar to a given query. The method is based on a query-by-example procedure whose inputs are evolution patterns provided by the end-user and outputs are other similar spatio-temporal patterns. The comparison between the temporal sequences and the queries is performed using the Dynamic Time Warping alignment method, whereas the separation between similar and non-similar patterns is determined via Expectation-Maximization. The experiments, which are assessed on both short and long SITS, prove the effectiveness of the proposed SITS retrieval method for different application scenarios. For the short SITS, we considered two application scenarios, namely the construction of two accumulation lakes and flooding caused by heavy rain. For the long SITS, we used a database formed of 88 Landsat images, and we showed that the proposed method is able to retrieve similar patterns of land cover and land use.
Highlights
Over the years, the remote sensing domain has been characterized by numerous technological improvements
These improvements were made possible through several Earth Observation missions, e.g., the Landsat program sustained by NASA and the United Stated Geological Survey (USGS), the Sentinel program financed by European Space Agency, Envisat’s ASAR mission
We described an effective query-by-example retrieval system that can be used for the exploitation of Earth Observation satellite image time series (SITS)
Summary
The remote sensing domain has been characterized by numerous technological improvements (e.g., increased spatial resolution, shorter revisit time, increased number of spectral bands). These improvements were made possible through several Earth Observation missions, e.g., the Landsat program sustained by NASA and the United Stated Geological Survey (USGS), the Sentinel program financed by European Space Agency, Envisat’s ASAR mission. One possible method for the analysis of satellite image time series (SITS) is to compare two satellite images captured at two successive moments of time, over the same area of interest [1,2,3,4] These methods are generally called change detection methods. They can successfully detect abrupt changes (e.g., deforestation, natural disasters, building construction), these methods are not able to identify complex spatio-temporal structures that evolve in a defined time-frame
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