深度学习在海珠国家湿地公园统一确权调查中的应用  

Application of deep learning in the unified verification and registration of natural resources rights of Haizhu National Wetland Park

作　　者：程晓晖[1] 李长辉[1] 欧佳斌 刘业光[1] CHENG Xiaohui;LI Changhui;OU Jiabin;LIU Yeguang(Guangzhou Urban Planning&Design Survey Research Institute,Guangzhou 510060,China)

机构地区：[1]广州市城市规划勘测设计研究院,广东广州510060

出　　处：《测绘通报》2021年第11期110-114,共5页Bulletin of Surveying and Mapping

基　　金：广东省重点领域研发计划(2020B0101130009)。

摘　　要：针对自然资源调查中单一数据源难以兼顾地物空间与光谱属性的问题,本文提出了基于深度学习的数据融合分类方法。该方法利用多源数据有限样本,实现了植被覆盖区精细化分类;并以广东海珠国家湿地公园为试点,完成了示范区内自然资源类型提取,以及植被数量与质量调查。试验结果表明,该方法可有效实现自然资源类型提取与植被分类,林木数量探测正确率优于87%,显著提升了植被分类精细度,探索了基于精细化调查的自然确权登记途径。To solve the problem that a single-source observation is hard to balance between the spatial and spectral properties of natural resources, this paper proposes a data-fusing classification method based on deep learning. Using limited samples of multi-source data, the method accomplishes the detailed classification of vegetation coverage areas, completes the extraction of natural resource types and the investigation of vegetation quantity in a pilot area, the Haizhu National Wetland Park in Guangdong province. The results show that this method can effectively extract natural resource types and classify vegetation. Its accuracy of forest number detection is better than 87%, which significantly improves the fineness of vegetation classification, and explores the way of detailed investigation of natural resources for ownership confirmation and registration.

关 键 词：自然资源 湿地公园 统一确权登记 深度学习 权籍调查 

分 类 号：P237[天文地球—摄影测量与遥感]