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SELF ATTENTION DEEP GRAPH CNN CLASSIFICATION OF TIMES SERIES IMAGES FOR LAND COVER MONITORING

Abstract : Time Series of Satellite Imagery (SITS) acquired by recent Earth observation systems represent an important source of information that supports several remote sensing applications related to monitoring the dynamics of the Earth's surface over large areas. A major challenge then is to design new deep learning models that can take into account intelligently the complementarity between temporal and spatial contexts that characterize these data structures. In this work, we propose to use an adapted self-attention convolutional neural network for spatio-temporal graphs classification that exploits both spatial and temporal dimensions. The graphs will be generated from a series of temporal images that are segmented into different regions. Those graphs are then classified using the Self-Attention Deep Graph CNN (DGCNN) model to highlight the temporal evolution of land cover areas through the construction of a spatio-temporal Map.
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https://hal.telecom-paris.fr/hal-03756739
Contributor : Florence Tupin Connect in order to contact the contributor
Submitted on : Monday, August 22, 2022 - 1:07:27 PM
Last modification on : Tuesday, August 30, 2022 - 3:07:12 AM
Long-term archiving on: : Wednesday, November 23, 2022 - 9:54:08 PM

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  • HAL Id : hal-03756739, version 1

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Ferdaous Chaabane, Safa Réjichi, Florence Tupin. SELF ATTENTION DEEP GRAPH CNN CLASSIFICATION OF TIMES SERIES IMAGES FOR LAND COVER MONITORING. IGARSS, 2022, Kuala Lumpur, Malaysia. ⟨hal-03756739⟩

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