耦合时序特征的林分类型遥感识别

Remote sensing classification of stand type coupled with time series features

【目的】结合多源遥感数据进行特征提取,获取最优分类策略并探究时间序列特征在林分类型识别中的重要性,为遥感林分类型识别提供技术途径。【方法】结合Sentinel-2光谱特征和时间序列特征、Sentinel-1雷达后向散射特征和SRTM DEM地形特征在Google Earth Engine中进行各特征变量的提取,构建不同特征组合使用随机森林分类器进行分类并对不同分类结果进行制图输出和精度评价。【结果】(1)使用Sentinel-2时间序列光谱特征、Sentinel-1雷达后向散射特征和SRTM DEM地形特征的方案分类效果最好,总体精度为84.62%,Kappa系数为0.82;(2)在构建的5个不同特征组合方案中,多特征组合的方案分类效果优于单一特征;(3)地形特征、后向散射特征和时间序列特征对于分类结果非常重要,尤其是时间序列特征的加入能大大提升林分类型识别精度。光谱特征中短红外波段B11和B12最重要,时间序列特征中4月份和10月份为最重要的时间节点。【结论】基于多源遥感数据提取的多特征分类方案能够有效进行研究区林分类型识别,地形特征、后向散射特征和Sentinel-2时间序列特征可以作为光谱特征的有效辅助特征变量提高分类精度,使林分类型识别更为准确,尤其是时间序列特征在提高林分类型识别精度上有突出作用。

[Objective]This paper aims to combine multi-source remote sensing data for feature extraction to determine the most effective classification strategy.Additionally,we investigated the significance of time series features in identifying forest types,offering a technical approach for remote sensing-based forest type identification.[Method]This study combined Sentinel-2 spectral features,time series features,Sentinel-1 radar backscatter features,and SRTM DEM terrain features to extract various feature variables using Google Earth Engine.Multiple feature combinations were constructed and classified using the random forest classifier.Subsequently,mapping output and accuracy evaluations were performed on the resulting classifications.[Result](1)The scheme that incorporates Sentinel-2 time series features,Sentinel-1 radar backscatter features,and SRTM DEM terrain features exhibited the highest classification accuracy,achieving an overall accuracy of 84.62%and a Kappa coefficient of 0.82.(2)Among the five constructed feature combination schemes,the multi-feature combination scheme demonstrated superior classification performance compared with individual feature.(3)Terrain features,radar backscatter features,and time series features significantly influenced the classification results.The inclusion of time series features notably enhanced the accuracy of forest type identification.Among the spectral features,the shortwave infrared bands B11 and B12 were the most critical,while April and October were identified as the most important time nodes within the time series features.[Conclusion]The multi-feature classification scheme,which combines data from various remote sensing sources,is proved to be effective in accurately identifying forest types in the study area.SRTM DEM terrain features,Sentinel-1 radar backscatter features,and Sentinel-2 time series features serve as valuable complementary indicators to spectral features,enhancing classification accuracy.Time series features,in particular,play a significant role in improving the accuracy of forest type identification.