Coastal Wetland Vegetation Classification Using Pixel-Based, Object-Based and Deep Learning Methods Based on RGB-UAV
Coastal Wetland Vegetation Classification Using Pixel-Based, Object-Based and Deep Learning Methods Based on RGB-UAV
复制标题
基于RGB-UAV的基于像素、基于对象和深度学习方法的滨海湿地植被分类
DOI：
10.3390/land11112039
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Bin Zhao
中科院分区：
文献类型：
--
作者：
Jun;Y. Hao;Yuan;Si;Wanben Wu;Qi Yuan;Yu Gao;Haiqiang Guo;Xingyu Cai;Bin Zhao
深度学习（DL）技术和无人机（UAV）遥感技术的进步使得高效准确地监测沿海湿地成为可能。然而，在基于无人机的沿海湿地监测中，研究很少将DL与传统机器学习（基于像素（PB）和基于对象的图像分析（OBIA）方法）的性能进行比较。基于RGB无人机数据构建了一个数据集，并比较了PB、OBIA和DL方法在滨海湿地植被群落分类中的性能。此外，本文基于Google Earth Engine（GEE）首次将OBIA方法应用于无人机数据，验证了GEE处理无人机数据的能力。结果表明，与PB和OBIA方法相比，DL方法的分类效果最好，能够反映植被的真实分布。此外，从PB和OBIA的范式转移到DL方法的特征工程，训练方法和参考数据解释了DL方法取得的可观的成果。结果表明，无人机、DL和云计算平台的组合可以促进在局部尺度上对沿海湿地植被进行长期、准确的监测。
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.