基于WorldView-2影像和语义分割模型的小麦分类提取

Winter Wheat Extraction of WorldView-2 Image based on Semantic Segmentation Method

为使用高分辨率遥感影像和深度学习语义分割模型实现快速准确的小麦种植空间信息提取,以WorldView-2遥感影像为数据源,制作尺度分别为128×128、256×256、512×512的样本数据集,对U-net和DeepLab3+语义分割模型的参数进行训练,建立小麦遥感分类模型;通过与极大似然和随机森林方法比较,检验深度学习分类效果。结果显示:(1)不同尺度样本训练得到的模型总体精度、Kappa系数分别在94%和0.82以上,模型精度稳定,样本尺度大小对小麦分类提取模型影响较小;(2)深度学习方法的小麦分类总精度和Kappa系数分别在94%和0.89以上,极大似然和随机森林则在92%和0.85以下,表明该研究建立的小麦遥感分类模型优于传统分类方法。研究结果可为高分辨率遥感影像作物种植信息的深度学习方法提取提供参考。

In order to realize fast and accurate extraction of winter wheat planting spatial information by using high-resolution remote sensing image and deep learning semantic segmentation model,worldView-2 remote sensing image was used as the data source to produce the sample data sets with the scales of 128×128,256×256 and 512×512,which trained the parameters of U-net and DeepLabv3+ semantic segmentation model to establish remote sensing classification model of winter wheat. The classification effects of deep learning was tested by comparing with maximum likelihood and random forest methods. The results showed that:(1)the overall accuracy and Kappa coefficient of the models obtained by training samples of different scales were more than94% and 0.82,and the model accuracy was stable,which indicated that the sample sizes have little influence on the semantic segmentation model of winter wheat classification.(2)The overall classification accuracy and Kappa coefficient of the deep learning methods were above 94% and 0.89,while the maximum likelihood and random forest were below 92% and 0.85,respectively. This results suggested that the remote sensing classification model of winter wheat established in this study was superior to the traditional classification methods. The results can provide the references for the deep learning methods of crop planting information extraction with high resolution remote sensing image.