Application of Deep Learning in Land Use Classification for Soil Erosion Using Remote Sensing - Wan, Lihong; Li, Shihua; Chen, Yao; He, Ze; Shi, Yanli (Frontiers in Earth Science, 2022)
In this study, deep learning semantic segmentation technology was applied to remote sensing dynamic monitoring of land use andsoil erosion.Interpretation mark samples of multistage remote sensing images of soil and water conservation and the results of land use classification in recent years were combined to constructtraining samples formachine learning.Adeep convolution neural network model based on ResNet152 and DeeplabV3+ was selected for repeated iterations. Finally, prediction classification wasperformed, and the automatic classification vector results were optimized.The experimental results show that the model can quickly extract robust edge features fromremote sensing images and has high classification accuracy. Although there are differences in geographical location and image data sources, the mean pixel accuracy of land use classification of the model as a whole can be more than 70%.It can further improve the application and information service level of soil and water conservation dynamic monitoring data and has some reference significance in automatic land use interpretation and classification of soil and water conservation monitoring remote sensing images ofmedium and high resolution.