Comparing machine learning techniques for aquatic vegetation classification using Sentinel-2 data

Erika Piaser, P. Villa
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引用次数: 1

Abstract

Wetlands, among the most valuable ecosystems, are increasingly threatened by anthropogenic impacts and climate change. Mapping wetland vegetation changes is crucial for conservation, management, and restoration of such sensitive environments. Machine Learning (ML) algorithms, such as Random Forest (RF), Support Vector Machine (SVM), kNearest Neighbour (kNN), and Artificial Neural Networks (ANN) are commonly applied for wetland mapping based on remote sensing data. However, scientific literature on this topic is often biased towards limited study areas, and lacking generalization testing over heterogeneous environmental conditions (e.g. latitude, ecoregion, wetland type). In this study, we compared eight ensemble and standalone ML methods, aiming at finding the best performing ones for aquatic vegetation mapping using Sentinel-2 over nine study areas and different seasons. The classifiers were tested to distinguish nine different classes - five aquatic vegetation classes and four background land cover classes – with seasonal monthly composites (April-November) of spectral indices as input. Results suggest that ensemble methods, such as RF, generally show higher predictive power with respect to most of common standalone classifiers (e.g. kNN or DT), which show the highest level of overall disagreement. SVM method overcame all the other classifiers, both standalone and ensemble, over our reference dataset, scoring an overall accuracy of 0.977 ± 0.001; In particular, SVM was the best over transitional aquatic vegetation classes (helophytes and submerged-floating association), which are the ones most frequently misclassified by other methods. Further developments of this research will focus on assessing the influence on classification performance of predictor variables and variations in input features.
比较基于Sentinel-2数据的水生植被分类机器学习技术
湿地作为最有价值的生态系统之一,正日益受到人为影响和气候变化的威胁。绘制湿地植被变化图对于湿地敏感环境的保护、管理和恢复至关重要。随机森林(Random Forest, RF)、支持向量机(Support Vector Machine, SVM)、最近邻(kNN, kNN)和人工神经网络(Artificial Neural Networks, ANN)等机器学习算法在基于遥感数据的湿地制图中得到了广泛应用。然而,关于这一主题的科学文献往往偏向于有限的研究领域,缺乏对异质环境条件(如纬度、生态区域、湿地类型)的泛化检验。在这项研究中,我们比较了8种集成和独立的ML方法,旨在找到在9个研究区域和不同季节使用Sentinel-2进行水生植被制图的最佳方法。以光谱指数的季节性月复合(4 - 11月)为输入,测试了分类器区分9个不同的类别——5个水生植被类别和4个背景土地覆盖类别。结果表明,相对于大多数常见的独立分类器(如kNN或DT),集成方法(如RF)通常显示出更高的预测能力,这显示出最高水平的总体分歧。在我们的参考数据集上,SVM方法克服了所有其他分类器,包括独立分类器和集成分类器,总体准确率为0.977±0.001;其中,支持向量机在过渡性水生植被类别(水生植物和浮潜联合)上的分类效果最好,而这两个类别是其他方法最容易错误分类的。本研究的进一步发展将集中于评估预测变量和输入特征变化对分类性能的影响。
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