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Morphological Hierarchies for Satellite Image Time Series

Abstract : Although morphological hierarchies are today a well-established framework for single frame image processing, their extension to time-related data remains largely unexplored. This thesis aims to tackle the analysis of satellite image time series with tree-based representations. To do so, we distinguish between three kinds of models, namely spatial, temporal and spatio-temporal hierarchies. For each model, we propose a streaming algorithm to update the tree when new images are appended to the series. Besides, we analyze the structural properties of the different tree building strategies, thus requiring some projection methods for the spatio-temporal tree in order to obtain comparable structures. Then, trees are compared according to their node distribution, filtering capability and cost, leading to a superiority of the spatio-temporal tree (a.k.a. space-time tree). Hence, we review spatio-temporal attributes, including some new ones, that can been extracted from the space-time tree in order to compute some multiscale features at the pixel or image level. These attributes are finally involved in tools such as filtering and pattern spectrum for various remote sensing based applications.
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Submitted on : Wednesday, November 24, 2021 - 12:28:11 PM
Last modification on : Friday, November 26, 2021 - 3:38:46 AM


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  • HAL Id : tel-03446274, version 1


Çaglayan Tuna. Morphological Hierarchies for Satellite Image Time Series. Traitement du signal et de l'image [eess.SP]. Université de Bretagne Sud, 2020. Français. ⟨NNT : 2020LORIS567⟩. ⟨tel-03446274⟩



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