Spatio-temporal reasoning for the classification of satellite image time series

Abstract : Satellite image time series (SITS) analysis is an important domain with various applications in land study. In the coming years, both high temporal and high spatial resolution SITS will become available. In the classical methodologies, SITS are studied by analyzing the radiometric evolution of the pixels with time. When dealing with high spatial resolution images, object-based approaches are generally used in order to exploit the spatial relationships of the data. However, these approaches require a segmentation step to provide contextual information about the pixels. Even if the segmentation of single images is widely studied, its generalization to series of images remains an open-issue. This article aims at providing both temporal and spatial analysis of SITS. We propose first segmenting each image of the series, and then using these segmentations in order to characterize each pixel of the data with a spatial dimension (i.e., with contextual information). Providing spatially characterized pixels, pixel-based temporal analysis can be performed. Experiments carried out with this methodology show the relevance of this approach and the significance of the resulting extracted patterns in the context of the analysis of SITS.
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François Petitjean, Camille Kurtz, Nicolas Passat, Pierre Gançarski. Spatio-temporal reasoning for the classification of satellite image time series. Pattern Recognition Letters, Elsevier, 2012, 33 (13), pp.1805-1815. ⟨10.1016/j.patrec.2012.06.009⟩. ⟨hal-01698140⟩

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