摘要
【目的】探究将简单非迭代聚类超像素分割算法(SNIC)融合到基于多时相数据的树种分类问题中,并对比分析不同时相数据组合对分类结果的影响,实现更高效、更精准的优势树种识别。【方法】以内蒙古旺业甸林场为研究区,在Google Earth Engine(GEE)云计算平台上利用多时相Sentinel-2多光谱数据提取波段反射率特征和光谱指数特征,采用SNIC和支持向量机(SVM)机器学习分类方法,实现面向对象的优势树种识别,并分析不同时相数据组合对优势树种识别精度的影响。【结果】多时相数据组合的分类精度明显高于各季节单时相数据。对比不同多时相数据组合分类结果,春、秋2个季节时间序列组合数据的分类精度与多季节组合数据结果相近,总体精度分别为94.5%、95.0%和95.8%。【结论】基于多时相Sentinel-2影像和SNIC分割算法的面向对象分类方法能够快速、准确识别优势树种,多季节组合数据的分类结果最优,春、秋2个季节时间序列数据也能获得较好分类结果,总体精度与最优结果差距较小。
【Objective】The spatial distribution of different tree species is the basis of forest inventory and forest dynamic monitoring, and is of great significance to the protection of forest ecosystems and the sustainable management of forest farms.【Method】In this paper, Wangyedian forest farm in Inner Mongolia is selected as the research area. Multi-temporal Sentinel-2 multi-spectral data is used on the Google Earth Engine(GEE)cloud computing platform to extract band reflectance characteristics and spectral index characteristics. The simple non-iterative clustering(SNIC)superpixel segmentation algorithm and the support vector machine(SVM)machine learning classification method are used to identify object-oriented dominant tree species, and the impact of different multi-temporal data combinations on the classification result is analyzed.【Result】Experimental result show that the classification accuracy of multi-temporal data combination is significantly higher than that of single-temporal data in each season. Comparing the multi-temporal data combination, The classification accuracy of the combined data of spring and autumn time series is similar to that of multi-season data combination, and their overall accuracy is 94.5%, 95.0%, 95.8%, respectively.【Conclusion】The object-oriented classification method proposed in this paper based on multi-temporal data and SNIC algorithm can identify dominant tree species quickly and accurately. Among them, the classification result using multi-season data combination is the best, and the time series data of the spring and autumn seasons can also obtain good classification result, and the overall accuracy is a little lower than the optimal result.
作者
岳巍
李世明
李增元
刘清旺
庞勇
斯林
Yue Wei;Li Shiming;Li Zengyuan;Liu Qingwang;Pang Yong;Si Lin(Key Laboratory of Forestry Remote Sensing and Information System,National Forestry and Grassland Administration Research Institute of Forest Resource Information Techniques,CAF,Beijing 100091)
出处
《林业科学》
EI
CAS
CSCD
北大核心
2022年第9期60-69,共10页
Scientia Silvae Sinicae
基金
中国林业科学研究院资源信息研究所结余资金项目(2019JYZJ05)
国家重点研发计划项目(2020YFE0200800)。
关键词
多时相
简单非迭代聚类超像素分割算法
树种识别
时间序列
multi-temporal
simple non-iterative clustering(SNIC)
tree species identification
time series
