双线性图卷积网络的环南海地区湿地遥感分类

doi:10.13474/j.cnki.11-2246.2023.0134

测绘通报 ›› 2023, Vol. 0 ›› Issue (5): 44-50.doi: 10.13474/j.cnki.11-2246.2023.0134

双线性图卷积网络的环南海地区湿地遥感分类

李心媛¹, 贺智^1,2, 楼桉君¹, 肖曼¹

1. 中山大学地理科学与规划学院, 广东广州 510275;
2. 南方海洋科学与工程广东省实验室(珠海), 广东珠海 519082

收稿日期:2022-05-23 发布日期:2023-05-31
通讯作者: 贺智。E-mail:hezh8@mail.sysu.edu.cn
作者简介:李心媛(1998-),女,硕士,主要研究方向为湿地遥感。E-mail:lixy245@mail2.sysu.edu.cn
基金资助:
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）

Remote sensing classification of wetlands in regions around the South China Sea based on bilinear graph convolutional network

LI Xinyuan¹, HE Zhi^1,2, LOU Anjun¹, XIAO Man¹

1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
2. Southern Marine Science and Engineering, Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

Received:2022-05-23 Published:2023-05-31

摘要/Abstract

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。

关键词: 湿地, 分类, 遥感, 深度学习, 环南海

Abstract: 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.

Key words: wetland, classification, remote sensing, deep learning, South China Sea

中图分类号:

P237

李心媛, 贺智, 楼桉君, 肖曼. 双线性图卷积网络的环南海地区湿地遥感分类[J]. 测绘通报, 2023, 0(5): 44-50.

LI Xinyuan, HE Zhi, LOU Anjun, XIAO Man. Remote sensing classification of wetlands in regions around the South China Sea based on bilinear graph convolutional network[J]. Bulletin of Surveying and Mapping, 2023, 0(5): 44-50.

参考文献

[1] MA K, LIU J. Changes in soil organic carbon stocks of wetlands on China's Zoige Plateau from 1980 to 2010[J]. Ecological Modelling, 2016, 327:18-28.
[2] 王勇辉. "21世纪海上丝绸之路"东南亚战略支点国家的构建[J]. 世界经济与政治论坛, 2016(3):61-73.
[3] 赵海迪, 刘世梁, 董世魁, 等. 三江源区人类干扰与湿地空间变化关系研究[J]. 湿地科学, 2014, 12(1):22-28.
[4] HE Zhi, LIU Han, WANG Yiwen, et al. Generative adversarial networks-based semi-supervised learning for hyperspectral image classification[J]. Remote Sensing, 2017, 9(10):1042.
[5] 贾文翰, 刘越岩. 基于卷积神经网络的高分遥感影像内陆盐沼湿地信息提取[J]. 测绘通报, 2021(6):89-92.
[6] JAMALI A, MAHDIANPARI M, MOHAMMADIMANESH F, et al. A synergic use of sentinel-1 and sentinel-2 imagery for complex wetland classification using generative adversarial network (GAN) scheme[J]. Water, 2021, 13(24):3601.
[7] 程晓晖, 李长辉, 欧佳斌, 等. 深度学习在海珠国家湿地公园统一确权调查中的应用[J]. 测绘通报, 2021(11):110-114.
[8] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL].[2016-01-01]. https://arxiv.org/abs/1609.02907.
[9] LIN T Y, ROYCHOWDHURY A, MAJI S. Bilinear CNN models for fine-grained visual recognition[C]//Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile:IEEE, 2016:1449-1457.
[10] 袁杰. 从海岸带到岛屿带:"环中国海"的地理与生态[J]. 南方文物, 2015(2):87-93.
[11] 刘云刚, 刘玄宇, 张争胜. 渔民视角下中国南海的领域构建[J]. 地理科学, 2020, 40(7):1062-1071.
[12] GONG Peng, LIU Han, ZHANG Meinan, et al. Stable classification with limited sample:transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017[J]. Science Bulletin, 2019, 64(6):370-373.
[13] BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1):5-32.
[14] HAMMOND D K, VANDERGHEYNST P, GRIBONVAL R. Wavelets on graphs via spectral graph theory[J]. Applied and Computational Harmonic Analysis, 2011, 30(2):129-150.
[15] CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3):273-297.
[16] 李朝奎, 方文, 董小姣. 面向对象和规则的高分辨率影像分类研究[J]. 测绘通报, 2015(9):9-13.

双线性图卷积网络的环南海地区湿地遥感分类

Remote sensing classification of wetlands in regions around the South China Sea based on bilinear graph convolutional network

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	张全旺, 郭辉. 采煤拉张裂隙区土壤有机质高光谱反演[J]. 测绘通报, 2023, 0(5): 78-83,134.
[2]	张适然, 陈明辉, 黄燕. 基于实景三维的东莞自建住宅提取与特征分析[J]. 测绘通报, 2023, 0(5): 120-124,163.
[3]	黄桦, 葛为燎, 刘微微, 钱荣荣, 李杰. 利用倾斜影像重建点云的建筑物变化检测[J]. 测绘通报, 2023, 0(5): 125-129.
[4]	吴斌, 王志勇, 李兴, 田康. CryoSat-2雷达高度计海冰波形优选特征分类[J]. 测绘通报, 2023, 0(5): 164-169.
[5]	王菡, 刘宁, 薛岩, 孙贵芹, 魏潇, 徐艳东. 1975—2020年烟台芝罘湾海湾形态变化及驱动因素[J]. 测绘通报, 2023, 0(4): 10-15.
[6]	邓敏, 王晶, 唐建波, 丁晨, 杨学习, 刘慧敏, 刘宝举. 顾及纹理特征差异性的遥感影像数据分区域多密级加密方法[J]. 测绘通报, 2023, 0(4): 23-28.
[7]	赵宗泽, 方明源, 高钊, 王双亭. 融合高程信息的低空遥感影像SLIC分割和区域合并方法[J]. 测绘通报, 2023, 0(4): 35-40.
[8]	邢若芸, 冉树浩, 高贤君, 杨元维, 方军. 顾及多分辨率特征的复合字典城中村识别方法[J]. 测绘通报, 2023, 0(4): 41-48,48.
[9]	蔡军, 温日强, 江伟, 严娇, 卢丽娟. 传统村落航拍图像中民族建筑识别的DeepLabV3+改进算法[J]. 测绘通报, 2023, 0(4): 49-53.
[10]	朱建伟, 李朝奎, 周新邵, 赵丁莹, 傅开鸿. 基于车载遥感影像的高速公路广告牌破损检测方法及应用[J]. 测绘通报, 2023, 0(4): 59-63,98.
[11]	陈宗杰, 杨武年, 张琬琳, 刘明鑫, 董秀军, 何先定. 基于植被冠层含水量反演数据的森林易燃程度评价[J]. 测绘通报, 2023, 0(4): 99-105.
[12]	齐建伟, 王伟峰, 张乐, 王光彦. 基于改进DeepLabV3+算法的遥感影像建筑物变化检测[J]. 测绘通报, 2023, 0(4): 145-149.
[13]	佘佐明, 申勇智, 宋剑虹, 向玉瑨. 利用经典CNN网络方法构建贵阳市道路要素遥感影像自动提取模型[J]. 测绘通报, 2023, 0(4): 177-182.
[14]	张悦, 张锦. 泥石流高分辨率遥感影像频域特征提取方法及应用[J]. 测绘通报, 2023, 0(3): 111-115.
[15]	邹业斌, 柴明堂, 杨娟, 申晓晶, 冯东溥, 舒瑞. 基于GEE的干旱区流域湿地水体时空动态变化特征分析--以宁夏平原为例[J]. 测绘通报, 2023, 0(2): 9-14.