基于非参数分类算法和多源遥感数据的单木树种分类
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摘要
【目的】通过研究随机森林(random forest, RF)特征筛选对单木树种分类精度的影响,以及多源遥感数据协同下单木树种分类的有效性,分析不同特征对单木树种分类的影响程度。【方法】以东北林业大学帽儿山实验林场中林施业区的两块100 m×100 m样地为研究对象,首先,以机载激光雷达(LiDAR,light detection and ranging)和多光谱遥感CCD(charge coupled device)影像为数据源,分别基于机载LiDAR数据提取高度、强度和树冠大小等共37个特征,基于CCD影像提取光谱和纹理共21个特征;其次,以随机森林方法进行特征筛选,之后以随机森林和支持向量机(support vector machine, SVM)两种非参数分类器,结合不同数据源和特征,采用12种分类方案,利用总体精度(overall accuracy, OA)、用户精度(user's accuracy, UA)和生产者精度(producer’s accuracy, PA)对分类结果进行对比与精度评价。【结果】经随机森林特征筛选后,分类结果优于未进行特征筛选的结果,总体精度可以平均提高3.47%,使用机载LiDAR和CCD影像协同分类相较于仅使用CCD影像总体精度平均提高6.07%。【结论】随机森林特征筛选可以优化特征,减少特征冗余,提高分类精度;多源数据结合也可以提高分类精度;在多源数据结合时,光谱特征最重要,LiDAR提取的强度特征相较于高度特征更稳定。
【Objective】 Forest vegetation is a principal part of forest resources. Accurate identification of forest vegetation types is important for research and utilization of forest resources. The combination of different characteristics of remote sensing data has great advantages for determining forest vegetation types and forest parameter estimation, which could be used to classify tree species more effectively. In the face of massive feature data, the use of classifiers that are insensitive to dimensionality could also result in a decrease in classification accuracy. At the same time, nonparametric classifiers [random forest(RF) and support vector machine(SVM)] will improve classification accuracy by adding non-spectral data to the classification process. In this study, the main impact of feature selection on classification results was explored, the importance of different features for tree species classification was studied, and the effectiveness of multi-source data for individual tree species classification was investigated. 【Method】 Two plots(100 m × 100 m) in the Zhonglin District of Maoershan Forest Farm of Northeast Forestry University, Heilongjiang Province, China, were used as the study area, multi-spectral remote sensing charge coupled device(CCD) images and airborne light detection and ranging(LiDAR) data were taken as data resources, and forest resource survey data from 2016 were taken as the basis of forest types classification system. First, LiDAR data were preprocessed and a canopy height model(CHM) was generated using the separated point cloud data. Then, CHM was optimized using the Khosravipour algorithm and individual tree crowns were segmented by region-based hierarchical cross-section analysis; subsequently, an accuracy assessment was performed. Second, 37 features, such as height, intensity and canopy size, were extracted based on airborne LiDAR data and a total of 21 texture and spectral features were extracted based on CCD images. Feature selection was performed using the RF method. Next, two kinds of nonparametric classifiers, including RF and SVM, were used for classification by combining with the segmented image object and selected features and 12 classification schemes. Finally, 40% of the data from each tree species were randomly selected to test the overall accuracy(OA), user's accuracy, and producer accuracy based on stratified sampling. Classification results were compared and evaluated. 【Result】 The detection accuracy of individual tree crown segmentation was over 80%, which conforms to forestry production requirements. In total, 12 features were retained using only airborne LiDAR data, 11 features were retained using only CCD images, and 11 features were retained by combining with the two datasets after RF feature selection. Then, the importance ranking of mean decrease accuracy was performed. After RF feature selection, the tree species classification results were better than those without RF feature selection. OA can be increased by an average of 3.47%. The average accuracy of combining CCD images and airborne LiDAR was increased by 6.07% compared with the average accuracy of using only CCD images. 【Conclusion】 RF feature selection could optimize features, reduce feature redundancy, and improve tree species classification accuracy. Multi-source data could also improve tree species classification accuracy. When combined with multi-source data, spectral features were the most important feature, and intensity features extracted from LiDAR data were more stable than height features. In the future, the combination of more band images and LiDAR data for different study areas will be considered and further studies will be conducted by adding more crown structure and spectral features.
【Objective】 Forest vegetation is a principal part of forest resources. Accurate identification of forest vegetation types is important for research and utilization of forest resources. The combination of different characteristics of remote sensing data has great advantages for determining forest vegetation types and forest parameter estimation, which could be used to classify tree species more effectively. In the face of massive feature data, the use of classifiers that are insensitive to dimensionality could also result in a decrease in classification accuracy. At the same time, nonparametric classifiers [random forest(RF) and support vector machine(SVM)] will improve classification accuracy by adding non-spectral data to the classification process. In this study, the main impact of feature selection on classification results was explored, the importance of different features for tree species classification was studied, and the effectiveness of multi-source data for individual tree species classification was investigated. 【Method】 Two plots(100 m × 100 m) in the Zhonglin District of Maoershan Forest Farm of Northeast Forestry University, Heilongjiang Province, China, were used as the study area, multi-spectral remote sensing charge coupled device(CCD) images and airborne light detection and ranging(LiDAR) data were taken as data resources, and forest resource survey data from 2016 were taken as the basis of forest types classification system. First, LiDAR data were preprocessed and a canopy height model(CHM) was generated using the separated point cloud data. Then, CHM was optimized using the Khosravipour algorithm and individual tree crowns were segmented by region-based hierarchical cross-section analysis; subsequently, an accuracy assessment was performed. Second, 37 features, such as height, intensity and canopy size, were extracted based on airborne LiDAR data and a total of 21 texture and spectral features were extracted based on CCD images. Feature selection was performed using the RF method. Next, two kinds of nonparametric classifiers, including RF and SVM, were used for classification by combining with the segmented image object and selected features and 12 classification schemes. Finally, 40% of the data from each tree species were randomly selected to test the overall accuracy(OA), user's accuracy, and producer accuracy based on stratified sampling. Classification results were compared and evaluated. 【Result】 The detection accuracy of individual tree crown segmentation was over 80%, which conforms to forestry production requirements. In total, 12 features were retained using only airborne LiDAR data, 11 features were retained using only CCD images, and 11 features were retained by combining with the two datasets after RF feature selection. Then, the importance ranking of mean decrease accuracy was performed. After RF feature selection, the tree species classification results were better than those without RF feature selection. OA can be increased by an average of 3.47%. The average accuracy of combining CCD images and airborne LiDAR was increased by 6.07% compared with the average accuracy of using only CCD images. 【Conclusion】 RF feature selection could optimize features, reduce feature redundancy, and improve tree species classification accuracy. Multi-source data could also improve tree species classification accuracy. When combined with multi-source data, spectral features were the most important feature, and intensity features extracted from LiDAR data were more stable than height features. In the future, the combination of more band images and LiDAR data for different study areas will be considered and further studies will be conducted by adding more crown structure and spectral features.
引文
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[6] 李春干,邵国凡.Landsat 7 ETM+图像用作SPOT5图像森林分类的辅助数据研究[J].北京林业大学学报,2010,32(4):1-5.LI C G,SHAO G F.Using Landsat 7 ETM+ images as ancillary data for forest cover classification of SPOT5 images[J].Journal of Beijing Forestry University,2010,32(4):1-5.
[7] 魏晶昱,毛学刚,方本煜,等.基于Landsat 8 OLI辅助的亚米级遥感影像树种识别[J].北京林业大学学报,2016,38(11):23-33.DOI:10.13332/j.1000-1522.20160054.WEI J Y,MAO X G,FANG B Y,et al.Submeter remote sensing image recognition of trees based on Landsat 8 OLI support[J].Journal of Beijing Forestry University,2016,38(11):23-33.
[8] HAN N,WANG K,YU L,et al.Integration of texture and landscape features into object-based classification for delineating Torreya using IKONOS imagery[J].International Journal of Remote Sensing,2012,33(7):2003-2033.DOI:10.1080/01431161.2011.605084.
[9] BLASCHKE T.Object based image analysis for remote sensing[J].ISPRS Journal of Photogrammetry and Remote Sensing,2010,65(1):2-16.DOI:10.1016/j.isprsjprs.2009.06.004.
[10] KE Y H,QUACKENBUSH L J,IM J.Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification[J].Remote Sensing of Environment,2010,114(6):1141-1154.DOI:10.1016/j.rse.2010.01.002.
[11] OLOFSSON K,WALLERMAN J,HOLMGREN J,et al.Tree species discrimination using Z/I DMC imagery and template matching of single trees[J].Scandinavian Journal of Forest Research,2006,21(S7):106-110.DOI:10.1080/14004080500486955.
[12] BUNTING P,LUCAS R.The delineation of tree crowns in Australian mixed species forests using hyperspectral compact airborne spectrographic imager (CASI) data[J].Remote Sensing of Environment,2006,101(2):230-248.DOI:10.1016/j.rse.2005.12.015.
[13] ZHEN Z,QUACKENBUSH L,ZHANG L J.Impact of tree-oriented growth order in marker-controlled region growing for individual tree crown delineation using airborne laser scanner (ALS) data[J].Remote Sensing,2014,6(1):555-579.DOI:10.3390/rs6010555.
[14] CHEN Q,BALDOCCHI D,GONG P,et al.Isolating individual trees in a savanna woodland using small footprint lidar data[J].Photogrammetric Engineering & Remote Sensing,2006,72(8):923-932.DOI:10.14358 /pers.72.8.923.
[15] ZHAO Y H,HAO Y S,ZHEN Z,et al.A region-based hierarchical cross-section analysis for individual tree crown delineation using ALS data[J].Remote Sensing,2017,9(10):1084.DOI:10.3390/rs9101084.
[16] DALPONTE M,BRUZZONE L,VESCOVO L,et al.The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas[J].Remote Sensing of Environment,2009,113(11):2345-2355.DOI:10.1016/j.rse.2009.06.013.
[17] PANG Y,LI Z Y,JU H B,et al.LiCHy:the CAF’s LiDAR,CCD and hyperspectral integrated airborne observation system[J].Remote Sensing,2016,8(5):398.DOI:10.3390/rs8050398.
[18] ZHAO D,PANG Y,LI Z Y,et al.Filling invalid values in a lidar-derived canopy height model with morphological crown control[J].International Journal of Remote Sensing,2013,34(13):4636-4654.DOI:10.1080/01431161.2013.779398.
[19] KHOSRAVIPOUR A,SKIDMORE A K,ISENBURG M,et al.Generating pit-free canopy height models from airborne lidar[J].Photogrammetric Engineering & Remote Sensing,2014,80(9):863-872.DOI:10.14358 /pers.80.9.863.
[20] ANDERSEN H E,MCGAUGHEY R J,REUTEBUCH S E.Estimating forest canopy fuel parameters using LIDAR data[J].Remote Sensing of Environment,2005,94(4):441-449.DOI:10.1016/j.rse.2004.10.013.
[21] LIN Y,HYYPP? J.A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification[J].International Journal of Applied Earth Observation and Geoinformation,2016,46:45-55.DOI:10.1016/j.jag.2015.11.010
[22] YAO W,KRZYSTEK P,HEURICH M.Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data[J].Remote Sensing of Environment,2012,123:368-380.DOI:10.1016/j.rse.2012.03.027.
[23] SHI Y F,WANG T J,SKIDMORE A K,et al.Important LiDAR metrics for discriminating forest tree species in Central Europe[J].ISPRS Journal of Photogrammetry and Remote Sensing,2018,137:163-174.DOI:10.1016 /j.isprsjprs.2018.02.002.
[24] ZHANG C Q,FRANKLIN S E,WULDER M A.Geostatistical and texture analysis of airborne-acquired images used in forest classification[J].International Journal of Remote Sensing,2004,25(4):859-865.DOI:10.1080/01431160310001618059.
[25] PAL M,FOODY G M.Feature selection for classification of hyperspectral data by SVM[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(5):2297-2307.DOI:10.1109 /tgrs.2009.2039484.
[26] 季金胜.高分辨率遥感影像典型地物目标的特征选择及其稳定性研究[D].上海:上海交通大学,2015.JI J S.Feature selection and its stability for typical geoobjects of the high-resolution remote sensing image[D].Shanghai:Shanghai Jiaotong University,2015.
[27] BAKSHI B R.Multiscale PCA with application to multivariate statistical process monitoring[J].AIChE Journal,1998,44(7):1596-1610.DOI:10.1002/aic.690440712.
[28] HASSANAT A B A,ESRA’N A,ALKAFAWEEN A.On enhancing genetic algorithms using new crossovers[J].International Journal of Computer Applications in Technology,2017,55(3):202.DOI:10.1504/ijcat.2017.084774.
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