随机森林方法支持的复杂地形区土地利用/土地覆被分类研究
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摘要
随机森林方法目前已经成为遥感分类机器学习中一种有效方法,探索基于中等分辨率的Landsat卫星数据与随机森林方法相结合对复杂地形区长时间序列数据的获取及土地利用/土地覆被变化及模拟研究是非常有意义的。本文基于Landsat8OLI卫星多光谱数据,采用随机森林分类方法对青海省湟水流域复杂地形区土地利用类型进行了分类研究。针对复杂地形区域的情况,将研究区进行地理分区,根据每个分区的特点,选择相应的地形特征参数,并通过提取Landsat 8数据的光谱信息与纹理信息构建最优特征集,探索随机森林方法在复杂地形区土地利用分类的适用性。结果表明:使用Landsat8OLI数据进行随机森林分类,能较好地得到湟水流域复杂地形区域的土地利用类型结果;光谱、地形及纹理信息的结合在不同分区的表现结果不同。在脑山区光谱与地形信息结合能使随机森林分类效果最佳,总体精度达到91.33%,Kappa系数为0.886;而在浅山区与川水区综合考虑光谱、地形、纹理信息进行随机森林分类效果最佳,浅山区与川水区总体精度分别达到92.09%和87.85%,Kappa系数分别为0.902和0.859;利用随机森林算法进行优化选择纹理特征组合可以在保证分类精度的同时能够快速地提取土地利用类型信息,为复杂地形区土地利用类型的区分提供了实际可行的方法。
Random forest classification has become an effective method in remote sensing classification of machine learning. It is of great significance to combine the Landsat satellite data and random forest method to obtain long time series data in the complex terrain areas and to explore its land use/land cover change. Based on the multi-spectral data of landsat8 OLI satellite, this paper adopted the random forest classification method to classify the land use types of Huangshui basin complex topography areas in Qinghai province. According to the characteristics of complex terrain areas, the study area was divided into different geographical regions. The topographic parameters were then selected, and the optimal feature collection was constructed by extracting spectral and texture information of Landsat8 data. The objective of this papers was to explore the applicability of random forest methods in land use classification on the complex topographic regions. The results showed that RFC classification with the landsat8 OLI data can be well used to obtain the land use types in the Huangshui basin. The combination of spectral, topographic, and texture information performed differently in different areas.In the middle and high mountain areas, the combination of spectral and topographic information can obtain the best results in the random forest classification with the overall accuracy of 91.33% and Kappa coefficient of0.886. In the shallow mountain areas and valley plain, however, the random forest classification can obtain the best results by combining spectral, topographic, and texture information with the overall accuracy of 92.09% and87.85% and Kappa coefficient of 0.902 and 0.859, respectively. Using the random forest algorithm to optimize the selection of texture feature combination can extract the land use type information quickly and ensure its accuracy. Random forest classification combined multi-source information can be used effectively to classify land use types, which can provide some enlightenment and reference values for the renewal of land use status and the development of social economy in the study area.
Random forest classification has become an effective method in remote sensing classification of machine learning. It is of great significance to combine the Landsat satellite data and random forest method to obtain long time series data in the complex terrain areas and to explore its land use/land cover change. Based on the multi-spectral data of landsat8 OLI satellite, this paper adopted the random forest classification method to classify the land use types of Huangshui basin complex topography areas in Qinghai province. According to the characteristics of complex terrain areas, the study area was divided into different geographical regions. The topographic parameters were then selected, and the optimal feature collection was constructed by extracting spectral and texture information of Landsat8 data. The objective of this papers was to explore the applicability of random forest methods in land use classification on the complex topographic regions. The results showed that RFC classification with the landsat8 OLI data can be well used to obtain the land use types in the Huangshui basin. The combination of spectral, topographic, and texture information performed differently in different areas.In the middle and high mountain areas, the combination of spectral and topographic information can obtain the best results in the random forest classification with the overall accuracy of 91.33% and Kappa coefficient of0.886. In the shallow mountain areas and valley plain, however, the random forest classification can obtain the best results by combining spectral, topographic, and texture information with the overall accuracy of 92.09% and87.85% and Kappa coefficient of 0.902 and 0.859, respectively. Using the random forest algorithm to optimize the selection of texture feature combination can extract the land use type information quickly and ensure its accuracy. Random forest classification combined multi-source information can be used effectively to classify land use types, which can provide some enlightenment and reference values for the renewal of land use status and the development of social economy in the study area.
引文
[1]杨超.基于混合像元分解决策树的土地利用/覆被分类研究[D].云南:云南师范大学,2017.[Yang C.Research on land use/land cover classification based on decision tree of mixed pixel decomposition[D].Yunnan:Yunnan normal university,2017.
[2]乔平林,张继贤,卢秀山,等.基于神经网络的区域生态环境分类方法研究[J].地理与地理信息科学,2004,20(2):97-99.[Qiao P L,Zhang J X,lu X S,et al.Regional ecological environment classification methods based on neural networks[J].Geographic and Geographic Information Science,2004,20(2):97-99.]
[3]徐凯健,田庆久,杨闫君,等.遥感土地覆被分类的空间尺度响应研究[J].地球信息科学学报,2018,20(2):246-253.[Xu K J,tian Q J,Yang Y J,et al.Research on spatial scale response of remote sensing land cover classification[J].Journal of Geo-information Science,2018,20(2):246-253.]
[4]杨雪峰,王雪梅.基于决策树的多角度遥感影像分类[J].地球信息科学学报,2016,18(3):416-422.[Yang X F,wang X M.Classification of multi-angle remote sensing images based on decision tree[J].Journal of Geoinformation Science,2016,18(3):416-422.]
[5]Pal M.Random forests for land cover classification[C]//Geoscience and Remote Sensing Symposium,2003.IGARSS'03.Proceedings of 2003 IEEE International,IEEE,2004(6):3510-3512.
[6]Chan C W,DesiréP.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.
[7]Waske B,Braun M.Classifier ensembles for land cover mapping using multitemporal SAR imagery[J].ISPRS Journal of Photogrammetry and Remote Sensing,2009,64(5):450-457.
[8]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.[Tian S F,Zhang X F.Classification of urban land cover in arid region using sky-painted data of random forest method[J].Remote Sensing for Land and Resources,2016,28(1):43-49.]
[9]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.[Ma Y,Jiang Q G,Meng Z G,et al.Land use classification in agricultural land based on random forest algorithm[J].Journal of Agricultural Machinery,2016,47(1):297-303.]
[10]韩燕,王玲,罗冲.随机森林算法在干旱区土地利用遥感分类中的应用研究[J].石河大学学报(自科版),2017,35(1):95-101.[Han Y,Wang L,Luo C.Application of random forest algorithm in remote sensing classification of land use in arid areas[J].Journal of Shihe University(Natural Science),2017,35(1):95-101.]
[11]谷晓天.基于机器学习的湟水流域土地利用/土地覆被分类研究[D].青海:青海师范大学,2018.[Gu X T.Research on land use/land cover classification based on machine learning in huangshui river basin[D].Qinghai.Qinghai normal university,2018.]
[12]曾永年,靳文凭,何丽丽,等.青海高原东部农业区土地利用遥感分类制图[J].农业工程学报,2012,28(16):225-231.[Zeng Y N,Jin W P,He L L,et al.Remote sensing classification mapping of land use in the eastern qinghai plateau agricultural region[J].Journal of Agricultural Engineering,2012,28(16):225-231.]
[13]熊有平.湟水流域川水区、浅山区、脑山区和石山林区划分及特点[J].水利科技与经济,2012,18(2):14-15.[Xiong Y P.Division and characteristics of valley plains,loess hills,middle and high mountains and stone mountain forests in huangshui river basin[J].Water Science and Technology and Economy,2012,18(2):14-15.]
[14]李金山.复杂地形区土地利用/土地覆被分类研究--以湟水流域为例[D].青海:青海师范大学,2013:1-82.[Li JS.A study on land use/land cover changes in complex topographical areas:A case study of the Qinshui river basin[D].Qinghai:Qinghai Normal University,2013:1-82.]
[15]贾伟,高小红,杨灵玉,等.面向对象方法的复杂地形区地表覆盖信息提取[J].兰州大学学报(自然科学版),2018,54(04):486-493.[Jia W,Gao X H,Yang L Y,et al.Surface cover information extraction of complex topographic areas by object-oriented method[J].Journal of Lanzhou University(Natural Science Edition),2008,54(04):486-493.]
[16]李欣海.随机森林模型在分类与回归分析中的应用[J].应用昆虫学报,2013,50(4):1190-1197.[Li xinhai.Application of stochastic forest model in classification and regression analysis[J].Chinese Journal of Applied Entomology,2013,50(4):1190-1197.]
[17]董师师,黄哲学.随机森林理论浅析[J].集成技术,2013,2(1):1-7.[Dong S S,Huang Z X.Analysis of random forest theory[J].Integrated Ttechnology,2013,2(1):1-7.]
[18]Menze B H,Kelm B M,Splitthoff D N,et al.On oblique random forests[C]//Machine Learning and Knowledge Discovery in Databases,Berlin:Springer,2011:453-469.
[19]Singh V P,Srivastava S,Srivastava R.Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests[J].Technology and Health Care,2016,25(4):709-727.
[20]王栋,岳彩荣,田传召,等.基于随机森林的大姚县TM遥感影像分类研究[J].林业调查规划,2014,39(2):1-5.[Wang D,Yue C R,Tian C Z,et al.Remote sensing image classification of dayao county based on random forest[J].Journal of Forestry Investigation and Planning,2014,39(2):1-5.]
[21]张大川,刘小平,姚尧,等.基于随机森林CA的东莞市多类土地利用变化模拟[J].地理与地理信息科学,2016,32(5):29-36.[Zhang D C,Liu X P,Yao Y,et al.Simulation of multiple land use changes in dongguan city based on stochastic forest CA[J].Geoscience and Geographic Information Science,2016,32(5):29-36.]
[22]白琳,徐永明,何苗,等.基于随机森林算法的近地表气温遥感反演研究[J].地球信息科学学报,2017,19(3):390-397.[Bai L,Xu Y M,He M,et al.Research on remote sensing inversion of near-surface temperature based on stochastic forest algorithm[J].Journal of Geoinformation Science,2017,19(3):390-397.]
[23]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,2012:1-130.[Lei Z.Application of random forest and its application in remote sensing image processing[D].Shanghai:Shanghai Jiao Tong University,2012:1-130.]
[24]Hernandez I E R,Shi W.A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis[J].International Journal of Remote Sensing,2018,39(4):1175-1198.
[2]乔平林,张继贤,卢秀山,等.基于神经网络的区域生态环境分类方法研究[J].地理与地理信息科学,2004,20(2):97-99.[Qiao P L,Zhang J X,lu X S,et al.Regional ecological environment classification methods based on neural networks[J].Geographic and Geographic Information Science,2004,20(2):97-99.]
[3]徐凯健,田庆久,杨闫君,等.遥感土地覆被分类的空间尺度响应研究[J].地球信息科学学报,2018,20(2):246-253.[Xu K J,tian Q J,Yang Y J,et al.Research on spatial scale response of remote sensing land cover classification[J].Journal of Geo-information Science,2018,20(2):246-253.]
[4]杨雪峰,王雪梅.基于决策树的多角度遥感影像分类[J].地球信息科学学报,2016,18(3):416-422.[Yang X F,wang X M.Classification of multi-angle remote sensing images based on decision tree[J].Journal of Geoinformation Science,2016,18(3):416-422.]
[5]Pal M.Random forests for land cover classification[C]//Geoscience and Remote Sensing Symposium,2003.IGARSS'03.Proceedings of 2003 IEEE International,IEEE,2004(6):3510-3512.
[6]Chan C W,DesiréP.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.
[7]Waske B,Braun M.Classifier ensembles for land cover mapping using multitemporal SAR imagery[J].ISPRS Journal of Photogrammetry and Remote Sensing,2009,64(5):450-457.
[8]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.[Tian S F,Zhang X F.Classification of urban land cover in arid region using sky-painted data of random forest method[J].Remote Sensing for Land and Resources,2016,28(1):43-49.]
[9]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.[Ma Y,Jiang Q G,Meng Z G,et al.Land use classification in agricultural land based on random forest algorithm[J].Journal of Agricultural Machinery,2016,47(1):297-303.]
[10]韩燕,王玲,罗冲.随机森林算法在干旱区土地利用遥感分类中的应用研究[J].石河大学学报(自科版),2017,35(1):95-101.[Han Y,Wang L,Luo C.Application of random forest algorithm in remote sensing classification of land use in arid areas[J].Journal of Shihe University(Natural Science),2017,35(1):95-101.]
[11]谷晓天.基于机器学习的湟水流域土地利用/土地覆被分类研究[D].青海:青海师范大学,2018.[Gu X T.Research on land use/land cover classification based on machine learning in huangshui river basin[D].Qinghai.Qinghai normal university,2018.]
[12]曾永年,靳文凭,何丽丽,等.青海高原东部农业区土地利用遥感分类制图[J].农业工程学报,2012,28(16):225-231.[Zeng Y N,Jin W P,He L L,et al.Remote sensing classification mapping of land use in the eastern qinghai plateau agricultural region[J].Journal of Agricultural Engineering,2012,28(16):225-231.]
[13]熊有平.湟水流域川水区、浅山区、脑山区和石山林区划分及特点[J].水利科技与经济,2012,18(2):14-15.[Xiong Y P.Division and characteristics of valley plains,loess hills,middle and high mountains and stone mountain forests in huangshui river basin[J].Water Science and Technology and Economy,2012,18(2):14-15.]
[14]李金山.复杂地形区土地利用/土地覆被分类研究--以湟水流域为例[D].青海:青海师范大学,2013:1-82.[Li JS.A study on land use/land cover changes in complex topographical areas:A case study of the Qinshui river basin[D].Qinghai:Qinghai Normal University,2013:1-82.]
[15]贾伟,高小红,杨灵玉,等.面向对象方法的复杂地形区地表覆盖信息提取[J].兰州大学学报(自然科学版),2018,54(04):486-493.[Jia W,Gao X H,Yang L Y,et al.Surface cover information extraction of complex topographic areas by object-oriented method[J].Journal of Lanzhou University(Natural Science Edition),2008,54(04):486-493.]
[16]李欣海.随机森林模型在分类与回归分析中的应用[J].应用昆虫学报,2013,50(4):1190-1197.[Li xinhai.Application of stochastic forest model in classification and regression analysis[J].Chinese Journal of Applied Entomology,2013,50(4):1190-1197.]
[17]董师师,黄哲学.随机森林理论浅析[J].集成技术,2013,2(1):1-7.[Dong S S,Huang Z X.Analysis of random forest theory[J].Integrated Ttechnology,2013,2(1):1-7.]
[18]Menze B H,Kelm B M,Splitthoff D N,et al.On oblique random forests[C]//Machine Learning and Knowledge Discovery in Databases,Berlin:Springer,2011:453-469.
[19]Singh V P,Srivastava S,Srivastava R.Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests[J].Technology and Health Care,2016,25(4):709-727.
[20]王栋,岳彩荣,田传召,等.基于随机森林的大姚县TM遥感影像分类研究[J].林业调查规划,2014,39(2):1-5.[Wang D,Yue C R,Tian C Z,et al.Remote sensing image classification of dayao county based on random forest[J].Journal of Forestry Investigation and Planning,2014,39(2):1-5.]
[21]张大川,刘小平,姚尧,等.基于随机森林CA的东莞市多类土地利用变化模拟[J].地理与地理信息科学,2016,32(5):29-36.[Zhang D C,Liu X P,Yao Y,et al.Simulation of multiple land use changes in dongguan city based on stochastic forest CA[J].Geoscience and Geographic Information Science,2016,32(5):29-36.]
[22]白琳,徐永明,何苗,等.基于随机森林算法的近地表气温遥感反演研究[J].地球信息科学学报,2017,19(3):390-397.[Bai L,Xu Y M,He M,et al.Research on remote sensing inversion of near-surface temperature based on stochastic forest algorithm[J].Journal of Geoinformation Science,2017,19(3):390-397.]
[23]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,2012:1-130.[Lei Z.Application of random forest and its application in remote sensing image processing[D].Shanghai:Shanghai Jiao Tong University,2012:1-130.]
[24]Hernandez I E R,Shi W.A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis[J].International Journal of Remote Sensing,2018,39(4):1175-1198.
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