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Coastal Wetland Vegetation Classification Using Pixel-Based, Object-Based and Deep Learning Methods Based on RGB-UAV

文献类型： 外文期刊

作者： Zheng, Jun-Yi 1 ; Hao, Ying-Ying 1 ; Wang, Yuan-Chen 1 ; Zhou, Si-Qi 1 ; Wu, Wan-Ben 1 ; Yuan, Qi 1 ; Gao, Yu 4 ; Guo, Hai-Qiang 1 ; Cai, Xing-Xing 1 ; Zhao, Bin 1 ;

作者机构： 1.Fudan Univ, Natl Observat & Res Stn Wetland Ecosyst Yangtze Es, Key Lab Biodivers Sci & Ecol Engn, Minist Educ, Shanghai 200433, Peoples R China

2.Fudan Univ, Shanghai Inst EcoChongming SIEC, Shanghai 200433, Peoples R China

3.UFZ Helmholtz Ctr Environm Res, Dept Urban & Environm Sociol, D-04318 Leipzig, Germany

4.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Minist Agr & Rural Affairs, Key Lab Fisheries Remote Sensing, Shanghai 200090, Peoples R China

5.Macquarie Univ, Sch Nat Sci, Sydney, NSW 2109, Australia

关键词： coastal wetlands; unmanned aerial vehicles; vegetation classification; deep learning; object-based image analysis (OBIA); Google Earth Engine (GEE)

期刊名称：LAND （ 影响因子：3.9； 五年影响因子：4.0 ）

ISSN：

年卷期： 2022 年 11 卷 11 期

页码：

收录情况： SCI

摘要： The advancement of deep learning (DL) technology and Unmanned Aerial Vehicles (UAV) remote sensing has made it feasible to monitor coastal wetlands efficiently and precisely. However, studies have rarely compared the performance of DL with traditional machine learning (Pixel-Based (PB) and Object-Based Image Analysis (OBIA) methods) in UAV-based coastal wetland monitoring. We constructed a dataset based on RGB-based UAV data and compared the performance of PB, OBIA, and DL methods in the classification of vegetation communities in coastal wetlands. In addition, to our knowledge, the OBIA method was used for the UAV data for the first time in this paper based on Google Earth Engine (GEE), and the ability of GEE to process UAV data was confirmed. The results showed that in comparison with the PB and OBIA methods, the DL method achieved the most promising classification results, which was capable of reflecting the realistic distribution of the vegetation. Furthermore, the paradigm shifts from PB and OBIA to the DL method in terms of feature engineering, training methods, and reference data explained the considerable results achieved by the DL method. The results suggested that a combination of UAV, DL, and cloud computing platforms can facilitate long-term, accurate monitoring of coastal wetland vegetation at the local scale.

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