摘要
湿地具有重要的碳汇功能,在净化水质、调节气候等方面起到关键作用。环南海地区湿地资源丰富,开展该地区湿地监测对推动我国滨海湿地的跨境联合保护,实现碳达峰、碳中和的目标具有重要意义。本文提出了一种基于双线性图卷积网络(BiGCN)的湿地分类方法,采用面向对象的分层分类思想,首先利用随机森林区分湿地与非湿地,然后构建BiGCN对湿地进行细化分类。在BiGCN中采用双线性模型、优化图结构、更优的激活函数等方式进一步优化网络性能。结果表明,在环南海地区3个Sentinel-2数据集上,本文提出的模型分类总体精度均大于92%,比现有图卷积网络提高了4%以上,且耗时大幅缩短。
Wetlands have important carbon sink functions,and play a key role in purifying water quality and regulating climate.There exist abundant wetland resources in regions around the South China Sea,and it is of great significance to monitor wetlands in this area to promote the cross-border joint protection of coastal wetlands in China and achieve the goal of carbon dioxide peaking and carbon neutrality.This paper proposes a wetland classification method based on BiGCN using object-oriented hierarchical classification.Random forest is used to distinguish wetlands from non-wetlands firstly,and then wetlands are sub-classified by the BiGCN.In the BiGCN,the methods of constructing bilinear model,optimizing graph structure and using better activation function are used to further optimize network performance.The results show that the overall classification accuracy of the proposed model is above 92%on the three Sentinel-2 data sets around the South China Sea,which is more than 4%higher than that of the existing graph convolution network,and the time consumption is greatly reduced.
作者
李心媛
贺智
楼桉君
肖曼
LI Xinyuan;HE Zhi;LOU Anjun;XIAO Man(School of Geography and Planning,Sun Yat-sen University,Guangzhou 510275,China;Southern Marine Science and Engineering,Guangdong Laboratory(Zhuhai),Zhuhai 519082,China)
出处
《测绘通报》
CSCD
北大核心
2023年第5期44-50,共7页
Bulletin of Surveying and Mapping
基金
国家重点研发计划(2020YFA0714103)
国家自然科学基金(42271325)
南方海洋科学与工程广东省实验室(珠海)创新团队建设项目(311022018)
广东省自然科学基金面上项目(2019A1515011877)
广州市科技计划(202002030240)
国防基础科研计划(WDZC20205500205)。
关键词
湿地
分类
遥感
深度学习
环南海
wetland
classification
remote sensing
deep learning
South China Sea
