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Article Dans Une Revue IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Année : 2020

Unsupervised Change Detection Analysis in Satellite Image Time Series using Deep Learning Combined with Graph-Based Approaches

Résumé

Nowadays, huge volume of satellite images, via the different Earth Observation missions, are constantly acquired and they constitute a valuable source of information for the analysis of spatio-temporal phenomena. However, it can be challenging to obtain reference data associated to such images to deal with land use or land cover changes as often the nature of the phenomena under study is not known a priori. With the aim to deal with satellite image analysis, considering a real-world scenario where reference data cannot be available, in this paper, we present a novel end-to-end unsupervised approach for change detection and clustering for satellite image time series (SITS). In the proposed framework, we firstly create bi-temporal change masks for every couple of consecutive images using neural network autoencoders. Then, we associate the extracted changes to different spatial objects. The objects sharing the same geographical location are combined in spatio-temporal evolution graphs that are finally clustered accordingly to the type of change process with gated recurrent unit (GRU) autoencoder-based model. The proposed approach was assessed on two real-world SITS data supplying promising results.
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Dates et versions

hal-02749683 , version 1 (03-06-2020)

Identifiants

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Ekaterina Kalinicheva, Dino Ienco, Jérémie Sublime, Maria Trocan. Unsupervised Change Detection Analysis in Satellite Image Time Series using Deep Learning Combined with Graph-Based Approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13, pp.1450-1466. ⟨10.1109/JSTARS.2020.2982631⟩. ⟨hal-02749683⟩
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