Research On Remote Sensing Information Extraction Algorithm Of Tiny Water Bodies Based On TransUNet

Small waterbody is a general term for waterbodies such as ditches,streams,canals,and ponds with a small area and a large number.Due to the influence of human activities and climate change,the spatial distribution and geometric characteristics of micro waterbodies change rapidly,which is helpful for the effective identification of micro waterbody information.For human beings to correctly understand the evolution and changes of waterbodies,it can provide scientific ideas and a basis for water resources management and ecological environmental protection.With the development of remote sensing technology and the improvement of sensor performance,high-resolution remote sensing images can show more details of ground objects,providing conditions for remote sensing monitoring of tiny waterbodies.However,the current high-resolution remote sensing image water extraction algorithm achieves good extraction results in large water bodies with simple features,but it has poor performance and a low degree of automation in the extraction of small waterbodies with different shapes,textures,and spectral characteristics.The remote sensing information extraction algorithm of small waterbodies under high-resolution satellites deserves further study.In this study,GF-2 images were used as the data source and carried out remote sensing information extraction of small waterbodies on the tributaries of Chaobai River in Langfang City.The hybrid coding Trans UNet was used as the baseline algorithm,and the attention mechanism,Vi T structure depth and coding were adopted for the characteristics of small water bodies.Length optimization and improvement The three optimization strategies of the encoder convolution layer are used to improve the algorithm.The semantic segmentation algorithm indicators such as precision rate,recall rate,overall accuracy,F1 score,and intersection ratio are used to evaluate the accuracy of waterbody extraction results.NDWI,SVM traditional waterbody extraction algorithm,Seg Net,U-Net,Deep Lab V3+ classic semantic segmentation algorithm and the original Trans UNet algorithm are compared,and finally the improved Trans UNet small water body extraction model is verified on the GID remote sensing dataset.The experimental results of small waterbody extraction show that the improved Trans UNet algorithm can extract the small waterbody with an accurate area and complete outline.94.94%and 90.37%,which are better than the original framework and other waterbody extraction models.The improved Trans UNet algorithm can accurately extract tiny waterbodies in complex scenes in the study area.In the experimental verification of the GID dataset,the five indicators of the improved Trans UNet algorithm accuracy rate,recall rate,overall accuracy rate,F1 score,and intersection ratio still hold 91.40%,90.69%,91.01%,91.05%,83.57% accuracy,the results show that the improved Trans UNet algorithm is robust and the algorithm is transferable.