摘要
【目的】对全卷积神经网络模型进行双支化改进,探索高空间分辨率遥感影像森林类型深度学习分类新方法,为提高森林资源遥感调查精度提供技术支撑。【方法】双支FCN-8s包含2个FCN-8s子模型,一个子模型基于R、G、B三波段特征,采用微调方式构建;另一个子模型基于五特征构建。将2个子模型8、16、32倍下的采样结果进行融合并分类,得到每个像元的类别。以旺业甸林场为研究区,采用GF-2卫星遥感影像提取标准化植被指数(NDVI),构建基于R+G+B三波段特征、R+G+B+NIR四波段特征和R+G+B+NIR+NDVI五特征的数据集,对双支FCN-8s优化方法的有效性进行定量评价。【结果】1)双支FCN-8s方法的总体分类精度为85.89%,Kappa系数为0.84;相比传统FCN-8s,双支FCN-8s方法可提高大部分森林类型的分类精度,尤其对油松、红松、白桦等类别改善效果明显。2)相对于传统基于特征优选的SVM模型而言,双支FCN-8s方法的总体分类精度由75%上升至85.89%,精度提升大于10%,各类别的分类效果均有改善。3)使用微调策略以及加入NDVI特征后,模型可有效改善油松、山杨及白桦等树种的分类效果。【结论】双支FCN-8s高空间分辨率遥感影像森林类型深度学习精细分类方法可有效提升森林类型的细分程度和分类精度。
【Objective】The study was conducted to perform the two-branch improvement on fully convolutional network models to explore a new method for the deep learning classification of remote sensing images of forest types with a high spatial resolution,with an aim to provide technical support for the improvement of the accuracy of remote sensing investigation of forest resources.【Method】The two-branch FCN-8s contained two FCN-8s sub-models.One sub-model was constructed by fine-tuning based on RGB three-band features and the other sub-model was constructed based on five-band features.The sampling results of these two sub-models at 8,16,and 32 magnifications were fused and classified to obtain the category of each pixel.Taking Wangyedian forest farm as the research area,the normalized difference vegetation index(NDVI)was extracted based on GF-2 satellite remote sensing images.In addition,the data set based on R+G+B three-band features,R+G+B+NIR four-band features and R+G+B+NIR+NDVI five-band features was constructed to evaluate the effectiveness of the two-branch FCN-8s optimization method quantitatively.【Result】1)The overall classification accuracy of the two-branch FCN-8s method is 85.89%and the Kappa coefficient is 0.84.Compared with the traditional FCN-8s,the dual-branch FCN-8s method can improve the classification accuracy of most forest types,especially for Chinese pine,Korean pine and white birch,and the improvement effects are significant.2)Compared with the SVM model based on traditional features,the overall classification accuracy of the dual-branch FCN-8s method increaseds from 75%to 85.89%.The accuracy is improved by over 10%.The classification effects of each category are also improved.3)With the fine-tuning strategy and NDVI features,the model can effectively improve the classification effects of Chinese pine,aspen and white birch.【Conclusion】The deep learning precision classification method for forest types with dual-branch FCN-8s high-resolution remote sensing images can effectively improve the subdivision degree and the classification accuracy of forest types.
作者
郭颖
李增元
陈尔学
张旭
赵磊
陈艳
王雅慧
Guo Ying;Li Zengyuan;Chen Erxue;Zhang Xu;Zhao Lei;Chen Yan;Wang Yahui(Research Institute of Forest Resource Information Techniques,CAF Beijing 100091)
出处
《林业科学》
EI
CAS
CSCD
北大核心
2020年第3期48-60,共13页
Scientia Silvae Sinicae
基金
国家重点研发计划“林业资源培育及高效利用技术创新人工林资源监测关键技术研究”(2017YFD0600900)
中央级公益性科研院所基本科研业务费专项“森林类型遥感分类的双支uNet优化组合模型研究”(CAFYBB2019SY027)。