Automatic classification of sand dune morphology based on convolutional neural networks
In order to address the issues of constructing a sand dune morphology dataset and developing an automatic classification method for sand dune morphology,especially in the context that there is few sand dune morphology information in existing databases,this study focuses on typical sand dunes in western Inner Mongolia to develop a method for classifying sand dunes according to their forms.Aerial images of six typical sand dune morphologies are collected using unmanned aerial vehicle orthoimage technology.Additionally,the sand dune morphology dataset is constructed by augmenting the data with GF-2 satellite remote sensing data.By utilizing the VGGNet and ResNet models with a transfer learning strategy,the deep semantic features of sand dune morphology are analyzed and learned,thus more representative texture features are automatically extracted.Based on this,a method for automatically classifying different sand dune morphological features using Convolutional Neural Networks(CNN)is proposed.The results show that the VGG16 model,based on transfer learning,achieves the highest classification accuracy among the four models,with an accuracy of 88.14％.The optimized ResNetl8 and ResNet50 models improve their classification accuracies from 84.04％and 85.25％to 92.79％and 88.91％,respectively.The optimized ResNetl8+model demonstrates the best performance,with an accuracy of 92.79％,making it more suitable for high-precision automated classification of sand dune morphology.
NETL
NSTL
万方数据
