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Preprints, Working Papers, ... Year : 2022

Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features

Abstract

An approach based on Gaussian Processes (GP) for land cover pixel-based classification with Sentinel-2 satellite image time-series (SITS) at national scale is proposed in this paper. Sparse methods combined with variational inference allow the learning of large scale GP. By using a spatio-spectro-temporal covariance function, our approach is able to use the spatial structure of the SITS in addition to the spectro-temporal structure. Experimental results conducted on 27 tiles on the south of France show that our method is effective and close to several state-of-the-art approaches (Random Forest and Deep Learning methods). Besides, the discontinuity between two spatially adjacent models independently trained is identified and quantified
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Dates and versions

hal-03781332 , version 1 (20-09-2022)
hal-03781332 , version 2 (05-01-2023)

Identifiers

  • HAL Id : hal-03781332 , version 1

Cite

Valentine Bellet, Mathieu Fauvel, Jordi Inglada. Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features. 2022. ⟨hal-03781332v1⟩
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