高分辨率多光谱遥感影像森林类型分类深度U-net优化方法

目的]使用深度学习全卷积神经网络U-net的自动特征提取,有效地改善遥感目标识别及地物分类的效果。方法]以内蒙古自治区赤峰市旺业甸林场为研究区,主要数据源包括GF-2多光谱数据、ZY-3 DOM数据、ZY-3DEM数据、小班数据以及外业实地调查数据等。借鉴前人对FCN-8s模型的优化思路,基于Unet网络模型,在模型训练过程中通过在原始波段的基础上加入标准归一化植被指数(NDVI)构建网络,并增加条件随机场后处理过程,得到最终的分类结果。结果]表明:(1)优化后的U-net模型的总体分类精度达84.89%,Kappa系数为0.82,分别高于未加入标准归一化植被指数特征的U-net模型以及未使用条件随机场进行后处理的U-net模型的分类精度;(2)优化后的U-net模型与使用相同策略的FCN-8s,支持向量机和随机森林的分类结果相比,提高了8.04%-12.54%,分类精度大幅度提高。结论]通过少量调整相关的遥感特征以及使用条件随机场后处理方法可改善U-net模型的分类效果,适用于基于U-net的森林类型高分辨率多光谱遥感影像分类。

Deep U-net Optimization Method for Forest Type Classification with High Resolution Multispectral Remote Sensing Images

Objective]Full convolution neural network U-net can effectively improve the effect of remote sensing target recognition and object classification.Method]The test site is located in Wangyedian Forest Farm,Chifeng district,Inner Mongolia Autonomous Region.The GF-2 multispectral data,ZY-3 DOM data,ZY-3 DEM data,subcompartment data and field survey data were employed as the key data sources.Based on the U-net network model and the optimization ideas of the previous FCN-8s model,the standard Nonnalize Different Vegetation Index(NDVI)was added to the original band in the training process,and the CRF post-processing process was added to construct the network and the final classification results were obtained.Result](1)The overall classification accuracy of the optimized U-net model was 84.89%,and the Kappa coefficient was 0.82,which was higher than that of the U-net model without NDVI feature and U-net model without CRF post-processing;(2)Compared with the classification results of FCN-8s,SVM and RF using the same strategy,the classification accuracy of the optimized U-net model was greatly improved.Conclusion]The classification effect of U-net model can be improved by adjusting the relevant remote sensing features and using CRF post-processing method.This method is suitable for the classification of high resolution multispectral remote sensing images of forest types.