随机森林遥感信息提取研究进展及应用展望
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摘要
针对国内外随机森林集成分类方法的相关成果及发展趋势尚未有研究进行梳理与展望这一问题,首先,系统介绍随机森林分类方法的基本原理及应用优势、重要参数及其具体设定;其次,综述该方法在多光谱影像、高光谱数据、雷达及激光测距仪等多源遥感数据信息提取领域以及分类参量遴选中的研究应用;最后,在分类精度检验、可移植性以及算法改进等方面对其发展及应用趋势进行了展望。该研究可为随机森林分类方法初学者提供参考,有助于随机森林分类方法在遥感信息提取领域的推广及应用。
The random forest classifier(RFC)is an ensemble method that produces multiple decision trees,using a randomly selected subset of training samples and feature variables.The classifier has become popular in remote sensing studies due to its classification accuracy,while no literature review has been done to cover its application in remote sensing.The objective of this study is to review the utilization of RFC in remote sensing,and the application of RFC in the classification of multi-sensor images and relevant data selection.Further investigations are recommended into less commonly exploited use of this classifier,such as outliers detecting in training samples and novel approaches improving.
The random forest classifier(RFC)is an ensemble method that produces multiple decision trees,using a randomly selected subset of training samples and feature variables.The classifier has become popular in remote sensing studies due to its classification accuracy,while no literature review has been done to cover its application in remote sensing.The objective of this study is to review the utilization of RFC in remote sensing,and the application of RFC in the classification of multi-sensor images and relevant data selection.Further investigations are recommended into less commonly exploited use of this classifier,such as outliers detecting in training samples and novel approaches improving.
引文
[1]赵萍,傅云飞,郑刘根,等.基于分类回归树分析的遥感影像土地利用/覆被分类研究[J].遥感学报,2005,9(6):708-716.
[2]曹铭昌,周广胜,翁恩生.广义模型及分类回归树在物种分布模拟中的应用与比较[J].生态学报,2005,25(8):2031-2040.
[3]谭琨,杜培军.基于支持向量机的高光谱遥感图像分类[J].红外与毫米波学报,2008,27(2):123-128.
[4]黄昕,张良培,李平湘.基于多尺度特征融合和支持向量机的高分辨率遥感影像分类[J].遥感学报,2007,11(1):48-54.
[5]骆剑承,周成虎,杨艳.人工神经网络遥感影像分类模型及其与知识集成方法研究[J].遥感学报,2001,5(2):122-129.
[6]贾永红.人工神经网络在多源遥感影像分类中的应用[J].测绘通报,2000(7):7-8.
[7]WOODCOCK C,STRAHLER A,JUPP D.The use of variograms in remote sensing:II.real digital images[J].Remote Sensing of Environment,1988,25(3):323-348.
[8]MARCEAU D,HOWARTH P,GRATTON D J.Remote sensing and the measurement of geographical entities in a forested environment.I.the scale and spatial aggregation problem[J].Remote Sensing of Environment,1994,49(2):105-117.
[9]BREIMAN L.Random forest[J].Machine Learning,2001,45:5-32.
[10]DU P,SAMAT A,WASKE B,et al.Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features[J].ISPRS Journal of Photogrammetry &Remote Sensing,2015,105:38-53.
[11]RODRIGUEZ-GALIANO V,GHIMIRE B,ROGAN J,et al.An assessment of the effectiveness of a random forest classifier for land-cover classification[J].ISPRS Journal of Photogrammetry &Remote Sensing,2012,67(1):93-104.
[12]KORTING T,FONSECA L,CAMARA G.GeoDMA:geographic data mining analyst[J].Computers &Geosciences,2013,57(57):133-145.
[13]GISLASON P,BENEDIKTSSON J,SVEINSSON J.Random forests for land cover classification[J].Pattern Recognition Letters,2006,27(4):294-300.
[14]GHOSH A,FASSNACHT F,JOSHI P,et al.A framework for mapping tree species combining hyperspectral and LiDAR data:role of selected classifiers and sensor across three spatial scales[J].International Journal of Applied Earth Observation &Geoinformation,2014,26(2):49-63.
[15]HAM J,CHEN Y,CRAWFORD M,et al.Investigation of the random forest framework for classification of hyperspectral data[J].IEEE Transactions on Geoscience &Remote Sensing,2005,43(3):492-501.
[16]翟云,杨炳儒,曲武,等.基于新型集成分类器的非平衡数据分类关键问题研究[J].系统工程与电子技术,2011,33(1):196-201.
[17]MIAO X,HEATON J,ZHENG S,et al.Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data[J].International Journal of Remote Sensing,2012,33(6):1823-1849.
[18]BREIMAN L.Bagging predictors[J].Machine Learning,1996,24(2):123-140.
[19]SCHAPIRE R.The strength of weak learnability[J].Proceedings of the Second Annual Workshop on Computational Learning Theory,1989,5(2):197-227.
[20]FREUNDY,SCHAPIRECHAPIRE R.A decision-theoretic generalization of on-line learning and an application to boosting[J].Journal of Computer &System Sciences,2010,55(1):119-139.
[21]BRIEM G,BENEDIKTSSON J,SVEINSSON J.Multiple classifiers applied to multisource remote sensing data[J].IEEE Transactions on Geoscience &Remote Sensing,2013,40(10):2291-2299.
[22]DIETTERICH T.An experimental comparison of three methods for constructing ensembles of decision trees:bagging,boosting,and randomization[J].Machine Learning,2000,40(2):139-157.
[23]XU L,LI J,BRENNING A.A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1imagery[J].Remote Sensing of Environment,2014,141(141):14-23.
[24]DEFRIES R,CHAN J.Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data[J].Remote Sensing of Environment,2000,74(3):503-515.
[25]顾海燕,闫利,李海涛,等.基于随机森林的地理要素面向对象自动解译方法[J].武汉大学学报(信息科学版),2016,41(2):228-234.
[26]KULKAMI V,SINHA P.Pruning of random forest classifiers:a survey and future directions[C]∥International Conference on Data Science &Engineering.IEEE,2012:64-68.
[27]GUAN H,LI J,CHAPMAN M,et al.Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests[J].International Journal of Remote Sensing,2013,34(14):5166-5186.
[28]LAWRENCE R,WOOD S,SHELEY R.Mapping invasive plants using hyperspectral imagery and breiman cutler classifications(randomforest)[J].Remote Sensing of Environment,2006,100(3):356-362.
[29]BELGIU M,DRraGUT L.Random forest in remote sensing:a review of applications and future directions[J].ISPRS Journal of Photogrammetry &Remote Sensing,2016,114:24-31.
[30]ABDE-RAHMAN E,MUTANGA O,ADAM E,et al.Detecting sirex noctilio,grey-attacked and lightning-struck pine trees using airborne hyperspectral data,random forest and support vector machines classifiers[J].ISPRS Journal of Photogrammetry &Remote Sensing,2014,88(2):48-59.
[31]MILLARD K,RICHARDSON M.On the importance of training data sample selection in random forest image classification:a case study in peatland ecosystem mapping[J].Remote Sensing,2015,7(7):8489-8515.
[32]NITZE I,BARRETT B,CAKWELL F.Temporal optimisation of image acquisition for land cover classification with random forest and MODIS time-series[J].International Journal of Applied Earth Observations &Geoinformation,2015,34(1):136-146.
[33]COLDITZ R.An evaluation of different training sample allocation schemes for discrete and continuous land cover classification using decision tree-based algorithms[J].Remote Sensing,2015,7(8):9655-9681.
[34]REESE H,Nystrom M,NORDKVIST K,et al.Combining airborne laser scanning data and optical satellite data for classification of alpine vegetation[J].International Journal of Applied Earth Observation &Geoinformation,2014,27(4):81-90.
[35]LIAW A,WIENER M.Classification and regression by random forest[J].R News,2002,2/3(23):18-22.
[36]OLIVEIRA S,OEHLER F,SAN-MIGUEL-AYANZ J,et al. Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and random forest[J].Forest Ecology &Management,2012,275(4):117-129.
[37]TATSUMI K,YAMASHIKI Y,TORRES M,et al.Crop classification of upland fields using random forest of time-series Landsat 7ETM+data[J].Computers &Electronics in Agriculture,2015,115:171-179.
[38]TSUTSUMIDA N,COMBER A.Measures of spatio-temporal accuracy for time series land cover data[J].International Journal of Applied Earth Observation &Geoinformation,2015,41:46-55.
[39]HAAS J,BAN Y.Urban growth and environmental impacts in Jing-Jin-Ji,the Yangtze,River Delta and the Pearl River Delta[J].International Journal of Applied Earth Observation &Geoinformation,2014,30(1):42-55.
[40]STEFANSKI J,MACK B,WASKE B.Optimization of object-based image analysis with random forests for land cover mapping[J].IEEE Journal of Selected Topics in Applied Earth Observations &Remote Sensing,2013,6(6):2492-2504.
[41]BELGIU M,LUCIAN D.Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery[J].ISPRS Journal of Photogrammetry &Remote Sensing Official Publication of the International Society for Photogrammetry &Remote Sensing,2014,96(4):67.
[42]RSNEN A,KUITUNEN M,LENSU A.What makes segmentation good?a case study in boreal forest habitat mapping[J].International Journal of Remote Sensing,2013,34(23):8603-8627.
[43]KARLSON M,OSTWALD M,REESE H,et al.Mapping tree canopy cover and aboveground biomass in sudano-sahelian woodlands using Landsat 8and random forest[J].Remote Sensing,2015,7(8):10017-10041.
[44]田绍鸿,张显峰.采用随机森林法的天绘数据干旱区城市土地覆盖分类[J].国土资源遥感,2016,28(1):43-49.
[45]刘毅,杜培军,郑辉,等.基于随机森林的国产小卫星遥感影像分类研究[J].测绘科学,2012,37(4):198-200.
[46]杜政,方耀.结合随机森林的高分一号分类最优组合研究[J].地理空间信息,2017,15(2):15-18.
[47]DENG C,WU C.The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques[J].ISPRS Journal of Photogrammetry &Remote Sensing,2013,86(4):100-110.
[48]WANG H,ZHAO Y,PU R,et al.Mapping robinia pseudoacacia forest health conditions by using combined spectral,spatial,and textural information extracted from Ikonos imagery and random forest classifier[J].Remote Sensing,2015,7(7):9020-9044.
[49]岳继博,杨贵军,冯海宽.基于随机森林算法的冬小麦生物量遥感估算模型对比[J].农业工程学报,2016,32(18):175-182.
[50]吴超凡.区域森林生物量遥感估测与应用研究[D].杭州:浙江大学,2016.
[51]XIA J,DU P,HE X,et al.Hyperspectral remote sensing image classification based on rotation forest[J].IEEE Geoscience &Remote Sensing Letters,2013,11(1):239-243.
[52]ZHANG C. Combining hyperspectral and lidar data for vegetation mapping in the Florida everglades[J].Photogrammetric Engineering &Remote Sensing,2014,80(8):733-743.
[53]韩兆迎,朱西存,房贤一,等.基于SVM与RF的苹果树冠LAI高光谱估测[J].光谱学与光谱分析,2016,36(3):800-805.
[54]程立真,朱西存,高璐璐,等.基于随机森林模型的苹果叶片磷素含量高光谱估测[J].果树学报,2016,33(10):1219-1229.
[55]郭澎涛,苏艺,茶正早.基于主成分分析和随机森林的橡胶小苗叶片磷含量高光谱预测[J].热带农业科学,2015,35(12):56-62.
[56]柴玉华,毕文佳,谭克竹,等.基于高光谱图像技术的大豆品种无损鉴别[J].东北农业大学学报,2016,47(3):86-93.
[57]张晓羽,李凤日,甄贞,等.基于随机森林模型的陆地卫星-8遥感影像森林植被分类[J].东北林业大学学报,2016,44(6):53-57.
[58]CAVALLARO G,MURA M,BENEDIKTSSON J,et al.Extended self-dual attribute profiles for the classification of hyperspectral images[J].IEEE Geoscience &Remote Sensing Letters,2016,12(8):1690-1694.
[59]李旭青,刘湘南,刘美玲,等.水稻冠层氮素含量光谱反演的随机森林算法及区域应用[J].遥感学报,2014,18(4):923-945.
[60]王婷.基于随机森林的高光谱遥感图像分类[J].数码世界,2017(5):37-38.
[61]LAWRENCE R,WOOD S,SHELEY R.Mapping invasive plants using hyperspectral imagery and breiman cutler classifications(random forest)[J].Remote Sensing of Environment,2006,100(3):356-362.
[62]PEERBHAY K,MUTANGA O,ISMAIL R.Random forests unsupervised classification:the detection and mapping of solanum mauritianum infestations in plantation forestry using hyperspectral data[J].IEEE Journal of Selected Topics in Applied Earth Observations &Remote Sensing,2015,8(6):3107-3122.
[63]ADELABU S,MUTANGA O,ADAM E,et al.Spectral discrimination of insect defoliation levels in mopane woodland using hyperspectral data[J].IEEE Journal of Selected Topics in Applied Earth Observations &Remote Sensing,2013,7(1):177-186.
[64]东朝霞,王迪,周清波,等.基于SAR遥感的北方旱地秋收作物识别研究[J].中国农业资源与区划,2016,37(8):27-36.
[65]UHLMANN S,KIRANYAZ S.Integrating color features in polarimetric SAR image classification[J].IEEE Transactions on Geoscience &Remote Sensing,2014,52(4):2197-2216.
[66]肖艳.面向对象的PolSAR影像分类研究[D].长春:吉林大学,2017.
[67]TOPOUZELIS K,PSYLLOS A.Oil spill feature selection and classification using decision tree forest on SAR image data[J].ISPRS Journal of Photogrammetry &Remote Sensing,2012,68(1):135-143.
[68]赖祖龙.基于LiDAR点云与影像的海岸线提取和地物分类研究[D].武汉:武汉大学,2013.
[69]SUN J,LAI Z.Airborne lidar feature selection for urban classification using random forests[J].Geomatics &Information Science of Wuhan University,2014,38:207-212.
[70]NIEMEYER J,ROTTENSTEINER F,SOERGEL U.Contextual classification of lidar data and building object detection in urban areas[J].ISPRS Journal of Photogrammetry &Remote Sensing,2014,87(1):152-165.
[71]鲁林,周小成,余治忠,等.随机森林算法在机载LiDAR数据林分平均树高估算中的应用研究[J].地球信息科学学报,2016,18(8):1133-1140.
[72]曹林,朱兴洲,代劲松,等.基于机载小光斑LiDAR数据插值亚热带森林下丘陵1地形的误差分析[J].南京林业大学学报(自然科学版),2014(4).
[73]CHEN W,LI X,WANG Y,et al.Forested landslide detection using lidar data and the random forest algorithm:a case study of the Three Gorges,China[J].Remote Sensing of Environment,2014,152:291-301.
[74]于丰华.基于无人机高光谱遥感的东北粳稻生长信息反演建模研究[D].沈阳:沈阳农业大学,2017.
[75]MA L,CHENG L,LI M,et al.Training set size,scale,and features in geographic object-based image analysis of very high resolution unmanned aerial vehicle imagery[J].ISPRS Journal of Photogrammetry &Remote Sensing,2015,102:14-27.
[76]SUN L,SCHULZ K.The improvement of land cover classification by thermal remote sensing[J].Remote Sensing,2015,7(7):8368-8390.
[77]王丽娜.基于马尔科夫链和随机森林方法的北美Chesapeake海湾环境因子对沉水植被与蓝蟹丰度影响的研究[D].烟台:烟台大学,2010.
[78]李大威,杨风暴,王肖霞.基于随机森林与D-S证据合成的多源遥感分类研究[J].激光与光电子学进展,2016(3):75-82.
[79]马玥,姜琦刚,孟治国,等.基于随机森林算法的农耕区土地利用分类研究[J].农业机械学报,2016,47(1):297-303.
[80]AHMED O,FRANKLIN S,WULDER M,et al.Characterizing stand-level forest canopy cover and height using landsat time series,samples of airborne lidar,and the random forest algorithm[J].ISPRS Journal of Photogrammetry &Remote Sensing,2015,101:89-101.
[81]CORCORAN J,KNIGHT J,GALLANT A.Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota[J].Remote Sensing,2013,5(7):3212-3238.
[82]ZHONG L,GONG P,BIGING G.Efficient corn and soybean mapping with temporal extend ability:a multi-year experiment using Landsat imagery[J].Remote Sensing of Environment,2014,140:1-13.
[83]VETRIVEL A,GERKE M,KERLE N,et al.Identification of damage in buildings based on gaps in 3Dpoint clouds from very high resolution oblique airborne images[J].ISPRS journal of photogrammetry and remote sensing,2015,105:61-78.
[84]JUEL A,GROOM G,SVENNING J,et al.Spatial application of random forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data[J].International Journal of Applied Earth Observation &Geoinformation,2015,42:106-114.
[85]BELGIU M,DR G.Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery[J].ISPRS Journal of Photogrammetry &Remote Sensing Official Publication of the International Society for Photogrammetry &Remote Sensing,2014,96(4):67.
[86]MISHRA N,CREWS K.Mapping vegetation morphology types in a dry savanna ecosystem:integrating hierarchical object-based image analysis with random forest[J].International Journal of Remote Sensing,2014,35(3):24.
[87]CHAN C,PAELINCKX D.Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery[J].Remote Sensing of Environment,2008,112(6):2999-3011.
[88]MELLOR A,BOUKIR S,HAYWOOD A,et al.Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin[J].ISPRS Journal of Photogrammetry &Remote Sensing,2015,105:155-168.
[89]雷震.随机森林及其在遥感影像处理中应用研究[D].上海:上海交通大学,2012.
[90]李贞贵.随机森林改进的若干研究[D].厦门:厦门大学,2013.
[91]ZHANG L,SUGANTHAN P N.Random forests with ensemble of feature spaces[J].Pattern Recognition,2014,47(10):3429-3437.
[2]曹铭昌,周广胜,翁恩生.广义模型及分类回归树在物种分布模拟中的应用与比较[J].生态学报,2005,25(8):2031-2040.
[3]谭琨,杜培军.基于支持向量机的高光谱遥感图像分类[J].红外与毫米波学报,2008,27(2):123-128.
[4]黄昕,张良培,李平湘.基于多尺度特征融合和支持向量机的高分辨率遥感影像分类[J].遥感学报,2007,11(1):48-54.
[5]骆剑承,周成虎,杨艳.人工神经网络遥感影像分类模型及其与知识集成方法研究[J].遥感学报,2001,5(2):122-129.
[6]贾永红.人工神经网络在多源遥感影像分类中的应用[J].测绘通报,2000(7):7-8.
[7]WOODCOCK C,STRAHLER A,JUPP D.The use of variograms in remote sensing:II.real digital images[J].Remote Sensing of Environment,1988,25(3):323-348.
[8]MARCEAU D,HOWARTH P,GRATTON D J.Remote sensing and the measurement of geographical entities in a forested environment.I.the scale and spatial aggregation problem[J].Remote Sensing of Environment,1994,49(2):105-117.
[9]BREIMAN L.Random forest[J].Machine Learning,2001,45:5-32.
[10]DU P,SAMAT A,WASKE B,et al.Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features[J].ISPRS Journal of Photogrammetry &Remote Sensing,2015,105:38-53.
[11]RODRIGUEZ-GALIANO V,GHIMIRE B,ROGAN J,et al.An assessment of the effectiveness of a random forest classifier for land-cover classification[J].ISPRS Journal of Photogrammetry &Remote Sensing,2012,67(1):93-104.
[12]KORTING T,FONSECA L,CAMARA G.GeoDMA:geographic data mining analyst[J].Computers &Geosciences,2013,57(57):133-145.
[13]GISLASON P,BENEDIKTSSON J,SVEINSSON J.Random forests for land cover classification[J].Pattern Recognition Letters,2006,27(4):294-300.
[14]GHOSH A,FASSNACHT F,JOSHI P,et al.A framework for mapping tree species combining hyperspectral and LiDAR data:role of selected classifiers and sensor across three spatial scales[J].International Journal of Applied Earth Observation &Geoinformation,2014,26(2):49-63.
[15]HAM J,CHEN Y,CRAWFORD M,et al.Investigation of the random forest framework for classification of hyperspectral data[J].IEEE Transactions on Geoscience &Remote Sensing,2005,43(3):492-501.
[16]翟云,杨炳儒,曲武,等.基于新型集成分类器的非平衡数据分类关键问题研究[J].系统工程与电子技术,2011,33(1):196-201.
[17]MIAO X,HEATON J,ZHENG S,et al.Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data[J].International Journal of Remote Sensing,2012,33(6):1823-1849.
[18]BREIMAN L.Bagging predictors[J].Machine Learning,1996,24(2):123-140.
[19]SCHAPIRE R.The strength of weak learnability[J].Proceedings of the Second Annual Workshop on Computational Learning Theory,1989,5(2):197-227.
[20]FREUNDY,SCHAPIRECHAPIRE R.A decision-theoretic generalization of on-line learning and an application to boosting[J].Journal of Computer &System Sciences,2010,55(1):119-139.
[21]BRIEM G,BENEDIKTSSON J,SVEINSSON J.Multiple classifiers applied to multisource remote sensing data[J].IEEE Transactions on Geoscience &Remote Sensing,2013,40(10):2291-2299.
[22]DIETTERICH T.An experimental comparison of three methods for constructing ensembles of decision trees:bagging,boosting,and randomization[J].Machine Learning,2000,40(2):139-157.
[23]XU L,LI J,BRENNING A.A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1imagery[J].Remote Sensing of Environment,2014,141(141):14-23.
[24]DEFRIES R,CHAN J.Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data[J].Remote Sensing of Environment,2000,74(3):503-515.
[25]顾海燕,闫利,李海涛,等.基于随机森林的地理要素面向对象自动解译方法[J].武汉大学学报(信息科学版),2016,41(2):228-234.
[26]KULKAMI V,SINHA P.Pruning of random forest classifiers:a survey and future directions[C]∥International Conference on Data Science &Engineering.IEEE,2012:64-68.
[27]GUAN H,LI J,CHAPMAN M,et al.Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests[J].International Journal of Remote Sensing,2013,34(14):5166-5186.
[28]LAWRENCE R,WOOD S,SHELEY R.Mapping invasive plants using hyperspectral imagery and breiman cutler classifications(randomforest)[J].Remote Sensing of Environment,2006,100(3):356-362.
[29]BELGIU M,DRraGUT L.Random forest in remote sensing:a review of applications and future directions[J].ISPRS Journal of Photogrammetry &Remote Sensing,2016,114:24-31.
[30]ABDE-RAHMAN E,MUTANGA O,ADAM E,et al.Detecting sirex noctilio,grey-attacked and lightning-struck pine trees using airborne hyperspectral data,random forest and support vector machines classifiers[J].ISPRS Journal of Photogrammetry &Remote Sensing,2014,88(2):48-59.
[31]MILLARD K,RICHARDSON M.On the importance of training data sample selection in random forest image classification:a case study in peatland ecosystem mapping[J].Remote Sensing,2015,7(7):8489-8515.
[32]NITZE I,BARRETT B,CAKWELL F.Temporal optimisation of image acquisition for land cover classification with random forest and MODIS time-series[J].International Journal of Applied Earth Observations &Geoinformation,2015,34(1):136-146.
[33]COLDITZ R.An evaluation of different training sample allocation schemes for discrete and continuous land cover classification using decision tree-based algorithms[J].Remote Sensing,2015,7(8):9655-9681.
[34]REESE H,Nystrom M,NORDKVIST K,et al.Combining airborne laser scanning data and optical satellite data for classification of alpine vegetation[J].International Journal of Applied Earth Observation &Geoinformation,2014,27(4):81-90.
[35]LIAW A,WIENER M.Classification and regression by random forest[J].R News,2002,2/3(23):18-22.
[36]OLIVEIRA S,OEHLER F,SAN-MIGUEL-AYANZ J,et al. Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and random forest[J].Forest Ecology &Management,2012,275(4):117-129.
[37]TATSUMI K,YAMASHIKI Y,TORRES M,et al.Crop classification of upland fields using random forest of time-series Landsat 7ETM+data[J].Computers &Electronics in Agriculture,2015,115:171-179.
[38]TSUTSUMIDA N,COMBER A.Measures of spatio-temporal accuracy for time series land cover data[J].International Journal of Applied Earth Observation &Geoinformation,2015,41:46-55.
[39]HAAS J,BAN Y.Urban growth and environmental impacts in Jing-Jin-Ji,the Yangtze,River Delta and the Pearl River Delta[J].International Journal of Applied Earth Observation &Geoinformation,2014,30(1):42-55.
[40]STEFANSKI J,MACK B,WASKE B.Optimization of object-based image analysis with random forests for land cover mapping[J].IEEE Journal of Selected Topics in Applied Earth Observations &Remote Sensing,2013,6(6):2492-2504.
[41]BELGIU M,LUCIAN D.Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery[J].ISPRS Journal of Photogrammetry &Remote Sensing Official Publication of the International Society for Photogrammetry &Remote Sensing,2014,96(4):67.
[42]RSNEN A,KUITUNEN M,LENSU A.What makes segmentation good?a case study in boreal forest habitat mapping[J].International Journal of Remote Sensing,2013,34(23):8603-8627.
[43]KARLSON M,OSTWALD M,REESE H,et al.Mapping tree canopy cover and aboveground biomass in sudano-sahelian woodlands using Landsat 8and random forest[J].Remote Sensing,2015,7(8):10017-10041.
[44]田绍鸿,张显峰.采用随机森林法的天绘数据干旱区城市土地覆盖分类[J].国土资源遥感,2016,28(1):43-49.
[45]刘毅,杜培军,郑辉,等.基于随机森林的国产小卫星遥感影像分类研究[J].测绘科学,2012,37(4):198-200.
[46]杜政,方耀.结合随机森林的高分一号分类最优组合研究[J].地理空间信息,2017,15(2):15-18.
[47]DENG C,WU C.The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques[J].ISPRS Journal of Photogrammetry &Remote Sensing,2013,86(4):100-110.
[48]WANG H,ZHAO Y,PU R,et al.Mapping robinia pseudoacacia forest health conditions by using combined spectral,spatial,and textural information extracted from Ikonos imagery and random forest classifier[J].Remote Sensing,2015,7(7):9020-9044.
[49]岳继博,杨贵军,冯海宽.基于随机森林算法的冬小麦生物量遥感估算模型对比[J].农业工程学报,2016,32(18):175-182.
[50]吴超凡.区域森林生物量遥感估测与应用研究[D].杭州:浙江大学,2016.
[51]XIA J,DU P,HE X,et al.Hyperspectral remote sensing image classification based on rotation forest[J].IEEE Geoscience &Remote Sensing Letters,2013,11(1):239-243.
[52]ZHANG C. Combining hyperspectral and lidar data for vegetation mapping in the Florida everglades[J].Photogrammetric Engineering &Remote Sensing,2014,80(8):733-743.
[53]韩兆迎,朱西存,房贤一,等.基于SVM与RF的苹果树冠LAI高光谱估测[J].光谱学与光谱分析,2016,36(3):800-805.
[54]程立真,朱西存,高璐璐,等.基于随机森林模型的苹果叶片磷素含量高光谱估测[J].果树学报,2016,33(10):1219-1229.
[55]郭澎涛,苏艺,茶正早.基于主成分分析和随机森林的橡胶小苗叶片磷含量高光谱预测[J].热带农业科学,2015,35(12):56-62.
[56]柴玉华,毕文佳,谭克竹,等.基于高光谱图像技术的大豆品种无损鉴别[J].东北农业大学学报,2016,47(3):86-93.
[57]张晓羽,李凤日,甄贞,等.基于随机森林模型的陆地卫星-8遥感影像森林植被分类[J].东北林业大学学报,2016,44(6):53-57.
[58]CAVALLARO G,MURA M,BENEDIKTSSON J,et al.Extended self-dual attribute profiles for the classification of hyperspectral images[J].IEEE Geoscience &Remote Sensing Letters,2016,12(8):1690-1694.
[59]李旭青,刘湘南,刘美玲,等.水稻冠层氮素含量光谱反演的随机森林算法及区域应用[J].遥感学报,2014,18(4):923-945.
[60]王婷.基于随机森林的高光谱遥感图像分类[J].数码世界,2017(5):37-38.
[61]LAWRENCE R,WOOD S,SHELEY R.Mapping invasive plants using hyperspectral imagery and breiman cutler classifications(random forest)[J].Remote Sensing of Environment,2006,100(3):356-362.
[62]PEERBHAY K,MUTANGA O,ISMAIL R.Random forests unsupervised classification:the detection and mapping of solanum mauritianum infestations in plantation forestry using hyperspectral data[J].IEEE Journal of Selected Topics in Applied Earth Observations &Remote Sensing,2015,8(6):3107-3122.
[63]ADELABU S,MUTANGA O,ADAM E,et al.Spectral discrimination of insect defoliation levels in mopane woodland using hyperspectral data[J].IEEE Journal of Selected Topics in Applied Earth Observations &Remote Sensing,2013,7(1):177-186.
[64]东朝霞,王迪,周清波,等.基于SAR遥感的北方旱地秋收作物识别研究[J].中国农业资源与区划,2016,37(8):27-36.
[65]UHLMANN S,KIRANYAZ S.Integrating color features in polarimetric SAR image classification[J].IEEE Transactions on Geoscience &Remote Sensing,2014,52(4):2197-2216.
[66]肖艳.面向对象的PolSAR影像分类研究[D].长春:吉林大学,2017.
[67]TOPOUZELIS K,PSYLLOS A.Oil spill feature selection and classification using decision tree forest on SAR image data[J].ISPRS Journal of Photogrammetry &Remote Sensing,2012,68(1):135-143.
[68]赖祖龙.基于LiDAR点云与影像的海岸线提取和地物分类研究[D].武汉:武汉大学,2013.
[69]SUN J,LAI Z.Airborne lidar feature selection for urban classification using random forests[J].Geomatics &Information Science of Wuhan University,2014,38:207-212.
[70]NIEMEYER J,ROTTENSTEINER F,SOERGEL U.Contextual classification of lidar data and building object detection in urban areas[J].ISPRS Journal of Photogrammetry &Remote Sensing,2014,87(1):152-165.
[71]鲁林,周小成,余治忠,等.随机森林算法在机载LiDAR数据林分平均树高估算中的应用研究[J].地球信息科学学报,2016,18(8):1133-1140.
[72]曹林,朱兴洲,代劲松,等.基于机载小光斑LiDAR数据插值亚热带森林下丘陵1地形的误差分析[J].南京林业大学学报(自然科学版),2014(4).
[73]CHEN W,LI X,WANG Y,et al.Forested landslide detection using lidar data and the random forest algorithm:a case study of the Three Gorges,China[J].Remote Sensing of Environment,2014,152:291-301.
[74]于丰华.基于无人机高光谱遥感的东北粳稻生长信息反演建模研究[D].沈阳:沈阳农业大学,2017.
[75]MA L,CHENG L,LI M,et al.Training set size,scale,and features in geographic object-based image analysis of very high resolution unmanned aerial vehicle imagery[J].ISPRS Journal of Photogrammetry &Remote Sensing,2015,102:14-27.
[76]SUN L,SCHULZ K.The improvement of land cover classification by thermal remote sensing[J].Remote Sensing,2015,7(7):8368-8390.
[77]王丽娜.基于马尔科夫链和随机森林方法的北美Chesapeake海湾环境因子对沉水植被与蓝蟹丰度影响的研究[D].烟台:烟台大学,2010.
[78]李大威,杨风暴,王肖霞.基于随机森林与D-S证据合成的多源遥感分类研究[J].激光与光电子学进展,2016(3):75-82.
[79]马玥,姜琦刚,孟治国,等.基于随机森林算法的农耕区土地利用分类研究[J].农业机械学报,2016,47(1):297-303.
[80]AHMED O,FRANKLIN S,WULDER M,et al.Characterizing stand-level forest canopy cover and height using landsat time series,samples of airborne lidar,and the random forest algorithm[J].ISPRS Journal of Photogrammetry &Remote Sensing,2015,101:89-101.
[81]CORCORAN J,KNIGHT J,GALLANT A.Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota[J].Remote Sensing,2013,5(7):3212-3238.
[82]ZHONG L,GONG P,BIGING G.Efficient corn and soybean mapping with temporal extend ability:a multi-year experiment using Landsat imagery[J].Remote Sensing of Environment,2014,140:1-13.
[83]VETRIVEL A,GERKE M,KERLE N,et al.Identification of damage in buildings based on gaps in 3Dpoint clouds from very high resolution oblique airborne images[J].ISPRS journal of photogrammetry and remote sensing,2015,105:61-78.
[84]JUEL A,GROOM G,SVENNING J,et al.Spatial application of random forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data[J].International Journal of Applied Earth Observation &Geoinformation,2015,42:106-114.
[85]BELGIU M,DR G.Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery[J].ISPRS Journal of Photogrammetry &Remote Sensing Official Publication of the International Society for Photogrammetry &Remote Sensing,2014,96(4):67.
[86]MISHRA N,CREWS K.Mapping vegetation morphology types in a dry savanna ecosystem:integrating hierarchical object-based image analysis with random forest[J].International Journal of Remote Sensing,2014,35(3):24.
[87]CHAN C,PAELINCKX D.Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery[J].Remote Sensing of Environment,2008,112(6):2999-3011.
[88]MELLOR A,BOUKIR S,HAYWOOD A,et al.Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin[J].ISPRS Journal of Photogrammetry &Remote Sensing,2015,105:155-168.
[89]雷震.随机森林及其在遥感影像处理中应用研究[D].上海:上海交通大学,2012.
[90]李贞贵.随机森林改进的若干研究[D].厦门:厦门大学,2013.
[91]ZHANG L,SUGANTHAN P N.Random forests with ensemble of feature spaces[J].Pattern Recognition,2014,47(10):3429-3437.