耦合Sentinel-1/2多源时序遥感影像特征的红树林物种分类方法

Classification of Mangrove Species Using Coupled Sentinel-1/2 Multi-Source Time-Series Remote Sensing Image Features

科学准确地监测红树林物种是保护滨海湿地生态系统的基础和前提。利用多源遥感数据能够有效进行红树林物种精细化分类,但在光学和SAR影像特征及其时间变化信息的应用方面仍存在一定的挑战。本文基于Sentinel-1/2影像数据,提出一种基于多源遥感影像特征耦合和集成学习(Ensemble Learning)算法(Multi-source Features-coupled and Ensemble Learning algorithm, MFEL)的红树林物种分类框架,通过分析光谱指数特征、雷达极化特征及其时序谐波谱特征在特征优选和耦合互补上的分类优势,堆叠随机森林(Random Forest, RF)和极端梯度提升(eXtreme Gradient Boosting, XGBoost)算法,构建红树林物种分类的集成学习分类模型,对比基于特征优选的RF分类模型和XGBoost分类模型,评估MFEL分类方法的分类精度和特征应用差异。本研究以湛江市红树林国家级自然保护区为实验区,实验结果表明:(1)相比于只使用光谱指数特征进行红树林物种分类而言,在增加雷达极化特征或时序谐波谱特征参与分类后,分类精度分别提高了6%和8%;同时增加雷达极化特征和时序谐波谱特征参与分类可以更精准地实现红树林物种分类,分类精度提高了12%;(2) MFEL方法分类精度最高,总体精度达到88.03%, Kappa系数为0.86;将使用实验区红树林物种样本训练的MFEL模型迁移至其他区域,物种分类精度分别为83.94%和82.77%;(3)研究验证了雷达极化特征和时序谐波谱特征在红树林物种分类中的应用潜力,对五种红树林物种分类的精度也有明显提升,分类精度为76%~91%。研究结果对拓展中分辨率遥感卫星影像进行红树林物种监测具有参考价值。

Scientific and accurate monitoring of mangroves is the basis and premise for protecting marine coastal wetland ecosystems.Multi-source remote sensing data can be used to classify mangrove species effectively,but challenges remain in applying optical and SAR image features along with their time-varying information.In this paper,based on Sentinel-1/2 image data,we propose a mangrove species classification framework using Multi-source Features-coupled and Ensemble Learning algorithm(MFEL).The framework analyzs the classification advantages of spectral index features,SAR polarization features,and their temporal harmonic spectral features in feature selection and coupling.It then stacks the Random Forest(RF)and eXtreme Gradient Boosting(XGBoost)models to construct an Ensemble Learning model for mangrove species classification.Comparing the RF classification model and XGBoost models based on feature optimization,we evaluated the classification accuracy and feature application differences of the MFEL classification method.Zhanjiang Mangrove Forest National Nature Reserve was selected as the experimental area.The results show that:①compared to using only spectral index features,classification accuracy improves by 6%and 8%with the addition of SAR polarization features or temporal harmonic spectral features,respectively.Adding both SAR polarization features and temporal harmonic spectral features simultaneously improves classification accuracy by 12%,making it more effective for mangrove species classification.②The MFEL method achieves the highest classification accuracy,with an overall accuracy of 88.03%and a Kappa coefficient of 0.86.When the MFEL model trained on samples from the experimental area was applied to other areas,the classification accuracies were 83.94%and 82.77%,respectively.③This study verifies the potential application of SAR polarization features and time-sequence harmonic spectral features in mangrove species classification,significantly improving the accuracy for five mangrove species,with accuracies ranging from 76%to 91%.The study results provide valuable insights for expanding the use of medium-resolution remote sensing satellite imagery in monitoring mangrove species.