生物多样性 ›› 2023, Vol. 31 ›› Issue (3): 22411.  DOI: 10.17520/biods.2022411

• 技术与方法 • 上一篇    下一篇

基于无人机影像与面向对象-深度学习的滨海湿地植物物种分类

黄雨菲1, 路春燕1,2,*(), 贾明明3, 王自立1, 苏越1, 苏艳琳1   

  1. 1.福建农林大学计算机与信息学院, 福州 350002
    2.福建农林大学生态与资源统计福建省高校重点实验室, 福州 350002
    3.中国科学院东北地理与农业生态研究所湿地生态与环境重点实验室, 长春 130102
  • 收稿日期:2022-07-19 接受日期:2022-11-07 出版日期:2023-03-20 发布日期:2023-02-22
  • 通讯作者: 路春燕
  • 作者简介:* E-mail: luchunyan@fafu.edu.cn
  • 基金资助:
    国家自然科学基金(42101392);福建省自然科学基金(2020J01572);福建农林大学杰出青年科研人才计划项目(XJQ201920);福建农林大学科技创新专项基金项目(CXZX2020106A)

Plant species classification of coastal wetlands based on UAV images and object- oriented deep learning

Yufei Huang1, Chunyan Lu1,2,*(), Mingming Jia3, Zili Wang1, Yue Su1, Yanlin Su1   

  1. 1 College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002
    2 Key Laboratory of Ecology and Resources Statistics in Higher Education Institutes of Fujian Province, Fujian Agriculture and Forestry University, Fuzhou 350002
    3 Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102
  • Received:2022-07-19 Accepted:2022-11-07 Online:2023-03-20 Published:2023-02-22
  • Contact: Chunyan Lu

摘要:

明确滨海湿地植物物种类型及其分布状况是实现滨海湿地精细化生物多样性监测的基础, 对于滨海湿地的保护管理与生态可持续发展均具有重要意义。本研究以无人机可见光遥感影像为基础数据源, 在定量分析最优分割尺度与最优分类特征组合的基础上, 应用面向对象-U-net深度学习方法对闽江河口湿地植物物种类型进行分类, 并与K最近邻、决策树、随机森林和贝叶斯分类方法进行精度对比分析, 以期为滨海湿地植物物种遥感精细分类与生物多样性保护管理提供方法借鉴与科学参考。研究结果表明, 利用面向对象-U-net深度学习方法提取不同滨海湿地植物物种类型的分类精度可达95.67%, 总体精度较其他分类方法提高6.67%-13.67%, Kappa系数提高0.12-0.31, 且分类整体性好。此外, 实现植物物种光谱特征、形状特征、纹理特征与高度特征的最优特征选择对于有效提高湿地植物物种信息分类精度具有重要作用, 应用最优分割尺度实现影像分割可提高整体分类效率。

关键词: 植物物种分类, 滨海湿地, 面向对象, U-net深度学习, 无人机遥感, 最优分割尺度, 机器学习

Abstract

Aims: Under the influence of high intensity human activities, e.g. urban expansion, offshore pollutant discharge and marine resources over-exploitation, various ecological problems have been caused in recent years, especially plant species decrease, productivity decline and ecological function degradation in coastal wetland. In this context, a quick and accurate understanding of types and distribution of plant species is of great significance to coastal wetland biodiversity conservation and ecological sustainable development. Due to cloud cover in coastal zones, it is difficult to obtain effective data by using optical satellite remote-sensed images. However, unmanned aerial vehicle (UAV) remote sensing technology can overcome weather constraints and provide intelligent and flexible data acquisition for a feasible technique for plant species monitoring in coastal zones. Compared with pixel-based classification methods, object- oriented classification method can effectively avoid the salt-pepper phenomenon with better classification performance. However, only the low-level features are applied to the object-oriented classification method. Because of this, it is difficult to improve the classification accuracy for complex regions with many plant species. High-level classification features are used by a deep learning method to identify land cover types to achieve higher classification accuracy. In this study, combing UAV remote sensing technology and object-oriented deep learning method, plant species information of coastal wetlands was identified and classified.

Methods: A representative coastal wetland area, located in Minjiang River estuary of South China, was chosen as the research site. High-resolution visible-light images of Minjiang River estuary wetland were obtained by UAV, and field sampling sites were collected by GPS. By the correction, splicing, and clipping of the UAV images, the digital orthophoto map and digital surface model were obtained. Then, on the basis of object-oriented multiresolution segmentation, the optimal segmentation scale of UAV images was determined by the scale parameter estimation model. The optimal feature combination was selected from spectral, texture, shape and height features based on separability analysis. The U-net deep learning method was used to extract the plant species information of coastal wetlands, and its classification accuracy was compared with four machine learning methods: K-nearest neighbor (KNN), decision tree (DT), random forest (RF) and Bayes.

Results: Combing object-oriented method and the U-net classification method, the classification results had the better integrity with less mixing and misclassification than other classification. The object-oriented U-net method could effectively avoid the salt-pepper phenomenon with overall accuracy (OA) 95.67% and the Kappa coefficient 0.91. The OA and Kappa coefficient of each classification were in descending order: U-net > Bayes > RF > DT > KNN. There were significant differences between the producer accuracy and user accuracy of a single plant species in different classification methods. Kandelia candel, Phragmites australis and Ipomoea pescaprae had higher identification accuracy, while Scirpus mariqueter and Cyperus malaccensis had lower identification accuracy.

Conclusion: The object-oriented U-net deep learning method has a favorable classification performance, and its accuracy is significantly higher than other methods in our study. The selection of optimal feature combination is the key to improving the extraction efficiency of coastal wetland plant species information. Our study could provide references for fine classification of coastal wetland plant species, as well as monitoring and biodiversity conservation management of coastal wetlands.

Key words: plant species classification, coastal wetland, object-oriented, U-net deep learning, unmanned aerial vehicle remote sensing, optimal segmentation scale, machine learning