U-Net深度卷积神经网络在沙脊线提取中的应用

Extracting the sand dune crest lines from satellite images using U-Net deep convolutional neural network

沙丘形态演变过程记录着近地表风况与环境演化的历史,然而对其特征研究一直受限于大范围沙脊线提取效率低和成本高等问题。本文基于深度卷积神经网络搭建U-Net模型,实现批量、高精度沙脊线的提取。将数据增强技术、随机失活神经元、批标准化以及迁移学习技术应用于模型训练和参数更新,使得模型的精度更高。结果表明:U-Net模型以及各种策略能够高效、精确地识别遥感影像中的沙脊线;沙脊线走向的偏移与近地表风况变化有着很好的对应关系,U-Net模型可以有效地用于区域性的沙脊线走向分析。

The evolution process of dune morphology records the history of near-surface wind conditions and environmental evolution,but its characteristic research has been limited by the inefficient and high cost of extracting large-scale dune crest lines.For this reason,this paper builds a U-Net model based on the deep convolutional neural network for batch and high-precision extraction.In order to obtain the best extraction results,the enhancement technology in data preprocessing,random neurons inactivation,batch normalization and transfer learning technology have been applied to the training and parameters updating,making the prediction accuracy of the model higher.The results show that the model and various strategies used in this paper can efficiently and accurately identify the dune crest lines in remote sensing images.In addition,through the application study of the orientation of the dune crest lines extracted from the trained model,we can find the shift of the dune crest lines orientation has a good correspondence with the change of near-surface wind regime,and it is confirmed that the U-Net model can be effectively used in regional dune orientation analysis.