Water extraction from SAR images based on TransUNet and its application in sandbar area change monitoring
Aiming at the challenges for high-precision water body extraction from synthetic aperture radar(SAR)images,the TransUnet deep learning model and high-resolution(3 m)data from COSMO-SkyMed satellites were used to delve into the global contextual capturing capabilities of the Transformer model and the multi-scale feature extraction advantages of the U-Net model in this paper.The water body extraction model for small sample datasets of SAR images was established.Experimental results revealed pronounced advantages of the proposed algorithm in extracting small-area water bodies and mitigating misclassifications in mountainous shadow regions.The accuracy,recall,overall accuracy,Fl score,and intersection over union(IoU)of the water body extraction results were reported as 89.54％,91.24％,98.01％,90.26％,and 82.28％,respectively.These results showed a noticeable improvement compared to the Unet,FCN-VGG16,and HRNet models.Concurrently,the proposed water body extraction model was utilized to infer the spatial distribution changes of sandbars in the Dongting Lake region across seven SAR images captured from July 2019 to June 2020.This elucidated the annual cyclical variation in response to seasonal water level changes.
high-resolutionSARdeep learningwater body extractionsandbar area changesTransformerU-Net
NETL
NSTL
万方数据
