As one of the most severe natural disasters in the world,floods caused substantial economic losses and casualties every year.Timely and accurate acquisition of flood inundation extent could provide technical support f...As one of the most severe natural disasters in the world,floods caused substantial economic losses and casualties every year.Timely and accurate acquisition of flood inundation extent could provide technical support for relevant departments in the field of flood emergency response and disaster relief.Given the accuracy of existing research works extracting flood inundation extent based on Synthetic Aperture Radar(SAR)images and deep learning methods is relatively low,this study utilized Sentinel-1 SAR images as the data source and proposed a novel model named flood water body extraction convolutional neural network(FWENet)for flood information extraction.Then three classical semantic segmentation models(UNet,Deeplab v3 and UNet++)and two traditional water body extraction methods(Otsu global thresholding method and Object-Oriented method)were compared with the FWENet model.Furthermore,this paper analyzed the water body area change situations of Poyang Lake.The main results of this paper were as follows:Compared with other five water body extraction methods,the FWENet model achieved the highest water body extraction accuracy,its F1 score and mean intersection over union(mIoU)were 0.9871 and 0.9808,respectively.This study could guarantee the subsequent research on flood extraction based on SAR images.展开更多
The Bi_4Ti_3O_(12)/g-C_3N_4 composites with microsheet and nanosheet structure were prepared through facile ultrasonic-assisted method. The SEM and TEM results suggested that the nanosheets g-C_3N_4 were stacked on th...The Bi_4Ti_3O_(12)/g-C_3N_4 composites with microsheet and nanosheet structure were prepared through facile ultrasonic-assisted method. The SEM and TEM results suggested that the nanosheets g-C_3N_4 were stacked on the surface of regular Bi_4Ti_3O_(12) sheets. Comparing with pure Bi_4Ti_3O_(12) and g-C_3N_4, the Bi_4Ti_3O_(12)/g-C_3N_4 composites showed significant enhancement in photocatalytic efficiency for the degradation of RhB in solution. With the mass ratio of g-C_3N_4 increasing to 10 wt%, the Bi_4Ti_3O_(12)/g-C_3N_4-10% presented the best photocatalytic activity. Its photocatalysis reaction constant was approximately 2 times higher than the single component Bi_4Ti_3O_(12) or g-C_3N_4. Meanwhile, good stability and durability for the Bi_4Ti_3O_(12)/g-C_3N_4-10% were confirmed by the recycling experiment and FT-IR analysis. The possible mechanism for the improvements was the matched band positions and the effective separation of photo-excited electrons(e-) and holes(h+). Furthermore, based on the results of active species trapping, photo-generated holes(h+) and superoxide radical(·O2-) could be the main radicals in reaction.展开更多
基金supported by Finance Science and Technology Project of Hainan Province[number ZDYF2021SHFZ103]and Strategic Priority Research Program of the Chinese Academy of Sciences[number XDA19090123].
文摘As one of the most severe natural disasters in the world,floods caused substantial economic losses and casualties every year.Timely and accurate acquisition of flood inundation extent could provide technical support for relevant departments in the field of flood emergency response and disaster relief.Given the accuracy of existing research works extracting flood inundation extent based on Synthetic Aperture Radar(SAR)images and deep learning methods is relatively low,this study utilized Sentinel-1 SAR images as the data source and proposed a novel model named flood water body extraction convolutional neural network(FWENet)for flood information extraction.Then three classical semantic segmentation models(UNet,Deeplab v3 and UNet++)and two traditional water body extraction methods(Otsu global thresholding method and Object-Oriented method)were compared with the FWENet model.Furthermore,this paper analyzed the water body area change situations of Poyang Lake.The main results of this paper were as follows:Compared with other five water body extraction methods,the FWENet model achieved the highest water body extraction accuracy,its F1 score and mean intersection over union(mIoU)were 0.9871 and 0.9808,respectively.This study could guarantee the subsequent research on flood extraction based on SAR images.
基金Supported by the National Natural Science Foundation of China(51509220)the Natural Science Foundation of Zhejiang Province(LQ14E090003)+1 种基金Ningbo Science and Technology Plan Projects(2014C50007,2014C51003)Ningbo major social development projects(2017C510006)
文摘The Bi_4Ti_3O_(12)/g-C_3N_4 composites with microsheet and nanosheet structure were prepared through facile ultrasonic-assisted method. The SEM and TEM results suggested that the nanosheets g-C_3N_4 were stacked on the surface of regular Bi_4Ti_3O_(12) sheets. Comparing with pure Bi_4Ti_3O_(12) and g-C_3N_4, the Bi_4Ti_3O_(12)/g-C_3N_4 composites showed significant enhancement in photocatalytic efficiency for the degradation of RhB in solution. With the mass ratio of g-C_3N_4 increasing to 10 wt%, the Bi_4Ti_3O_(12)/g-C_3N_4-10% presented the best photocatalytic activity. Its photocatalysis reaction constant was approximately 2 times higher than the single component Bi_4Ti_3O_(12) or g-C_3N_4. Meanwhile, good stability and durability for the Bi_4Ti_3O_(12)/g-C_3N_4-10% were confirmed by the recycling experiment and FT-IR analysis. The possible mechanism for the improvements was the matched band positions and the effective separation of photo-excited electrons(e-) and holes(h+). Furthermore, based on the results of active species trapping, photo-generated holes(h+) and superoxide radical(·O2-) could be the main radicals in reaction.
