基于U-Net模型的多时相Sentinel-2A/B影像林分类型分类  

Forest Type Classification Based on Multi-temporal Sentinel-2A/B Imagery Using U-Net Model

作　　者：杨丹 李崇贵[1] 李斌 YANG Dan;LI Chong-gui;LI Bin(College of Geomatics,Xi’an University of Science and Technology,Xi’an 710054,Shaanxi,China)

机构地区：[1]西安科技大学测绘科学与技术学院,陕西西安710054

出　　处：《林业科学研究》2022年第4期103-111,共9页Forest Research

基　　金：国家重点研发计划项目(2017YFD0600400)。

摘　　要：[目的]基于多时相Sentinel-2A/B影像,探究深度学习模型在森林植被上的分类效果。[方法]以黑龙江省孟家岗林场为研究区,以多时相Sentinel-2A/B影像、数字高程模型(DEM)为数据源,通过各森林类别的JM距离,确定最佳单一时相。同时,构建多时相植被指数及红边指数特征(DVI、mNDVI、CIred-edge、NDre1)。采用支持向量机和优化的U-Net模型分别对单一时相+DEM和单一时相+DEM+多时相植被指数两种方案进行分类实验。[结果](1)在单一时相+DEM基础上,加入多时相植被指数后,U-Net模型精度为77.87%,比单一时相+DEM精度高6.67%;(2)U-Net模型的总体精度明显优于支持向量机,并且分类效果更好。同时,深度学习U-Net模型能够避免“椒盐”现象,分类结果更细腻。[结论]基于多时相Sentinel-2A/B影像,构建植被指数及红边指数时序特征,同时采用U-Net模型在一定程度上能够提高林分类型分类精度。[Objective]To explore the classification effect of deep learning models on forest vegetation using multi-temporal Sentinel-2A/B images.[Method]In this study,based on the multi-temporal Sentinel-2A/B images and Digital Elevation Model(DEM)in Mengjiagang Forest Farm in Heilongjiang Province,the JM distance of each forest category was used to determine the best single-phase.The characteristics of multi-temporal vegetation index and red edge index(DVI,mNDVI,CIred-edge,NDre1)were analyzed.Support vector machine and optimized U-Net model were used to carry out classification experiments on single-phase+DEM and single-phase+DEM+multi-temporal vegetation index respectively.[Result](1)On the basis of single-phase+DEM,when adding multi-phase vegetation index,the accuracy of U-Net model was 76.37%,which was 5.7%higher than that of single-phase+DEM;(2)The accuracy of U-Net model was higher than that of support vector machine.In addition,the deep learning U-Net model could avoid the"salt and pepper"phenomenon,and the classification results were more delicate.[Conclusion]Based on multi-temporal Sentinel-2A/B images,the vegetation index and red edge index time series characteristics are constructed,and the U-Net model can improve the classification accuracy of forest types to a certain extent.

关 键 词：多时相Sentinel-2A/B影像 植被指数 红边指数 U-Net模型 支持向量机 森林分类 

分 类 号：X87[环境科学与工程—环境工程] S757[农业科学—森林经理学]