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Wetland is an important ecosystem and plays an important role in maintaining regional ecological security. However, the wetland structure changes respond sensitively to natural and human activities, and the flood wetlands experience drastic seasonal water and vegetation changes due to intermittent flood inundations. It is difficult to map the high-accuracy wetland structures as the frequent water and vegetation alternations lead to obvious spectral confusion and misclassification in optical satellite images. Mapping high-accuracy wetland structures is challenging due to frequent water and vegetation alternations, which cause spectral confusion and misclassification in optical satellite images. Recent studies suggest that deep learning semantic segmentation methods show great potential for mapping wetland changes in high-resolution images. In this study，a multi-scene and multi-temporal wetland sample dataset was collected using Sentinel-2 remote sensing images in the Taitam Lake wetland of Xinjiang, China. The D-UNet network was improved by replacing the convolution block before dimensionality reduction with multi-scale dilated convolutions to enhance the network"s receptive field, fuse features of different scales, and avoid loss of detailed information in high-resolution remote sensing images. The applicability of the improved D-UNet model, traditional index-thresholding methods, and four classical semantic segmentation networks for extracting wetland structural information in floodplains was compared. The results showed that the improved D-UNet network had an overall accuracy of 96.3% in single-temporal image wetland structure extraction. Moreover, it demonstrated better transferability on time-series images, with a multi-temporal overall accuracy of 92.3%. Compared to five models and the index-thresholding method, the improved D-UNet model showed better application potential in the extraction of floodplain wetland structure. It reduced misclassification and omission of wetland water bodies and vegetation by 7.2% and 48.9% compared to the index-thresholding method, and by 0.6% and 5.4% compared to D-UNet, respectively.