Extracting Water Body Information from High-resolution Remote Sensing Images by DeepLab V3+
Timely and accurate acquisition and mastery of water body distribution information are of great significance for water area management.This study takes the Hangjiahu water network plain in northern Zhejiang as the study area,and uses the random forest model and the Dilated Residual Networks(DRN)based DeepLab V3+model to extract water body information from Beijing No.2 high-resolution remote sensing images,and compares the results of the two models.The results show that the extraction accuracy of the random forest model using 5 features is better than the random forest model using only 3 features;the extraction accuracy of the DeepLab V3+model is significantly better than the random forest model that its overall extraction accuracy is 0.976 6,and the kappa coefficient is 0.826 6,MIoU is 0.826 6,both are significantly higher than the forest model;taking the visual interpretation results as a reference,the extraction results of DeepLab V3+are also significantly better than the random forest model,eliminating the obvious"salt and pepper effect"in the random forest extraction results.The reason may be related to the fact that the DeepLab V3+model can make full use of the spectral and spatial texture characteristics of high-resolution remote sensing images.Therefore,the DeepLabV3+model can effectively extract water body information from high-resolution remote sensing images,even in complex environments such as the Hangjiahu Plain,providing an effective way and means to quickly and accurately obtain water body distribution information.
万方数据
维普
