Research on Semantic Segmentation of Flood Inundation Areas in SAR Images
This paper studies the backscattering characteris-tics of SAR images of water and non-water,and focuses on the two key issues of automatic annotation and enhanced train-ing strategy.Threshold segmentation,hydrologic constraint and Markov random fields(MRF)are used in designing the automatic labeling algorithm using,with the integration of the feature enhancement network and embedded sample enhance-ment,resulting in a semantic segmentation method for SAR images with limited samples.In this study,the"22·6"Beiji-ang extreme flood is taken as an experimental case,and the GF-3 images of Pajiang River detention area are used as the experimental data.According to the experimental results,it is evident that the proposed method is capable of distinguishing water from non-water with 92.6%overall accuracy.
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