摘要
针对全波形激光雷达和多光谱数据下土地覆盖误分类问题,提出了融合陆地卫星(Landsat)多光谱遥感影像数据和星载全波形激光雷达全球生态系统动态调查(GEDI)数据进行土地覆盖分类的方法。首先,根据实地调查数据建立数据集;然后,采用支持向量机(SVM)方法来实现激光雷达足迹的土地覆盖分类;最后,对土地覆盖的分类结果进行评价。结果表明,在SVM方法下联合使用光谱特征和波形特征的总体准确率可以达到90.68%,相比仅使用光谱特征或波形特征时总体准确率可以提升8个百分点以上。融合光谱特征和波形特征的方法可以提高土地覆盖分类的准确性。
Objective Changes in land cover types lead to numerous ecological and environmental issues.For effective resolution of these issues,monitoring changes in land cover is crucial.Numerous studies have explored the use of full-waveform LiDAR for land cover classification.However,its accuracy can diminish with increasing classification categories.Enhancing classification accuracy necessitates the integration of auxiliary features with waveform features.Research indicates that combining waveform features with the spectral features of optical images can enhance the precision of land cover classification.Given the broad distribution of GEDI data across Earth s surface,it is valuable to investigate if merging GEDI waveform features with optical image spectral features positively impacts land cover classification.Improved accuracy could expand training/validation samples,particularly in regions with limited field surveys or high-resolution remote sensing data.This could enrich the sample pool for land cover classification tasks,thereby boosting overall classification accuracy.Methods A support vector machine(SVM)was used to classify footprints.First,the GEDI L2A footprint points in the study area in 2020 were extracted,and survey data were used to label the ground object categories under the footprint points.Simultaneously,the spectral reflectance of Landsat remote sensing images at the footprint points was extracted and the vegetation index was calculated.Second,the waveform information and spectrum at the footprint point were normalized,and the sample data were randomly divided into training and verification datasets.Among them,70%of the training data were used to train the SVM classification model,and 30%were used to verify its accuracy of the classification model.Next,the feature vectors in the training dataset were input into the SVM classification model,and GridSearchCV was used to search for the penalty coefficient and kernel function of the SVM to obtain the optimal parameters and train the optimal classification m
作者
黄兴
胡旭嫣
刘微微
赵宏
Huang Xing;Hu Xuyan;Liu Weiwei;Zhao Hong(Lishui Institute of Territorial Spatial Planning and Mapping,Lishui 323000,Zhejiang,China;Zhejiang South Comprehensive Engineering Survey and Mapping Institute Co.,Hangzhou 310030,Zhejiang,China;Zhejiang Academy of Surveying and Mapping Science and Technology,Hangzhou 311121,Zhejiang,China;College of Aeronautics,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,Jiangsu,China)
出处
《中国激光》
EI
CAS
CSCD
北大核心
2024年第8期214-222,共9页
Chinese Journal of Lasers
基金
自然资源部国土卫星遥感应用重点实验室开放基金(KLSMNR-K202205)
浙江省基础公益研究计划(LTGS23D010003)
浙江省自然资源厅2023年度自然资源科技项目(2023-20)。
