Multimodal and Multitemporal Land Use/Land Cover Semantic Segmentation on Sentinel-1 and Sentinel-2 Imagery: An Application on a MultiSenGE Dataset
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27 décembre 2022
- handle: 10670/1.b9pen7
- hal: hal-04339255
- doi: 10.3390/rs15010151
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info:eu-repo/semantics/altIdentifier/doi/10.3390/rs15010151
http://creativecommons.org/licenses/by-nc/ , info:eu-repo/semantics/OpenAccess
Mots-clés
multitemporal multimodal Sentinel-1 Sentinel-2 land use land cover deep learning time series multitemporal multimodal Sentinel-1 Sentinel-2 land use land cover deep learning time seriesSujets proches
Land Land utilization Utilization of land Use of land Cover, LandCiter ce document
Romain Wenger et al., « Multimodal and Multitemporal Land Use/Land Cover Semantic Segmentation on Sentinel-1 and Sentinel-2 Imagery: An Application on a MultiSenGE Dataset », HAL-SHS : géographie, ID : 10.3390/rs15010151
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Résumé
In the context of global change, up-to-date land use land cover (LULC) maps is a major challenge to assess pressures on natural areas. These maps also allow us to assess the evolution of land cover and to quantify changes over time (such as urban sprawl), which is essential for having a precise understanding of a given territory. Few studies have combined information from Sentinel-1 and Sentinel-2 imagery, but merging radar and optical imagery has been shown to have several benefits for a range of study cases, such as semantic segmentation or classification. For this study, we used a newly produced dataset, MultiSenGE, which provides a set of multitemporal and multimodal patches over the GrandEst region in France. To merge these data, we propose a CNN approach based on spatio-temporal and spatio-spectral feature fusion, ConvLSTM+Inception-S1S2. We used a U-Net base model and ConvLSTM extractor for spatio-temporal features and an inception module for the spatio-spectral features extractor. The results show that describing an overrepresented class is preferable to map urban fabrics (UF). Furthermore, the addition of an Inception module on a date allowing the extraction of spatio-spectral features improves the classification results. Spatio-spectrotemporal method (ConvLSTM+Inception-S1S2) achieves higher global weighted F1 Score than all other methods tested.