基于特征空间优化的随机森林算法在GF-2影像湿地分类中的研究
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摘要
由于季节性的植被动态和水文波动,湿地遥感影像分类常常比较困难。本文采用优化特征空间的随机森林算法(Random Forest)对吉林省白城市通榆县东部地区预处理后的GF-2影像进行湿地分类研究,具体分为2步:(1)对研究区遥感影像进行多尺度分割和对象特征的提取。针对一些学者获取最佳分割尺度时仍受主观因素影响较大的情况,本文通过改进全局最优分割方法来获得最佳分割尺度。(2)在最优分割的基础上,基于特征重要性对随机森林分类算法的特征空间进行优化,以得到最佳的随机森林分类结果,并与相同条件下(同数据、同分割尺度、同训练样本,同特征空间)的K-NN、SVM、CART 3种算法以及未优化特征空间的RF算法的分类结果进行了比较。结果表明,基于优化特征空间的RF算法的分类结果总精度和Kappa系数分别为93.038%和0.9177,而K-NN、SVM和CART 3种分类算法的分类结果的总精度分别为83.357%、78.068%、77.136%,未优化特征空间的RF算法分类结果总精度为90.937%。相较于K-NN、SVM、CART 3种分类算法,RF算法在GF-2湿地影像数据中具有更好的分类性能,同时优化特征空间的RF算法精度有所提高,在湿地资源管理中可以发挥非常重要的作用。
Due to seasonal vegetation dynamics and hydrological fluctuations, classification of wetland from remote sensing images is often more difficult. In this paper, a pretreated GF-2 image in the east of Tongyu Country, Baicheng City, Jilin Province, was analyzed by Random Forest with optimized feature space. The key method is divided into two steps. The first step is to perform multi-scale segmentation and extraction of object features in the remote sensing image of the study area. For a situation that some scholars obtain the best segmentation scale subjected to subjective factors, this paper obtains the best segmentation scale by improving the global optimal segmentation method, The second step is based on optimal segmentation, to optimize the feature space of the random forest classification algorithm on the basis of the importance of features to obtain the best random forest classification results, and then the classification results of the K-NN, SVM, and CART algorithms with the same data, the same segmentation scale, the same training sample and the same feature space, and the RF algorithm with unoptimized feature space are compared. The results show that the total classification accuracy and Kappa coefficient of the RF algorithm based on optimized feature space are 93.038%and 0.9177, respectively, while the total accuracy of the classification results of K-NN, SVM and CART are83.357% and 78.068%, respectively, 77.136%, the total accuracy of the classification results of RF algorithm with unoptimized feature space is 90.937%. Compared with K-NN, SVM and CART classification algorithms,the RF algorithm has better classification performance in GF-2 wetland image data. At the same time, the accuracy of the RF algorithm with the optimized feature space has been improved, and it can play a very important role in wetland resource management.
Due to seasonal vegetation dynamics and hydrological fluctuations, classification of wetland from remote sensing images is often more difficult. In this paper, a pretreated GF-2 image in the east of Tongyu Country, Baicheng City, Jilin Province, was analyzed by Random Forest with optimized feature space. The key method is divided into two steps. The first step is to perform multi-scale segmentation and extraction of object features in the remote sensing image of the study area. For a situation that some scholars obtain the best segmentation scale subjected to subjective factors, this paper obtains the best segmentation scale by improving the global optimal segmentation method, The second step is based on optimal segmentation, to optimize the feature space of the random forest classification algorithm on the basis of the importance of features to obtain the best random forest classification results, and then the classification results of the K-NN, SVM, and CART algorithms with the same data, the same segmentation scale, the same training sample and the same feature space, and the RF algorithm with unoptimized feature space are compared. The results show that the total classification accuracy and Kappa coefficient of the RF algorithm based on optimized feature space are 93.038%and 0.9177, respectively, while the total accuracy of the classification results of K-NN, SVM and CART are83.357% and 78.068%, respectively, 77.136%, the total accuracy of the classification results of RF algorithm with unoptimized feature space is 90.937%. Compared with K-NN, SVM and CART classification algorithms,the RF algorithm has better classification performance in GF-2 wetland image data. At the same time, the accuracy of the RF algorithm with the optimized feature space has been improved, and it can play a very important role in wetland resource management.
引文
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[5] Chen Y, He X, Wang J. Classification of coastal wetlands in eastern China using polarimetric SAR data[J]. Arabian Journal of Geosciences, 2015,8(12):10203-10211.
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[14]乔婷,张怀清,陈永富,等.基于NDVI分割与面向对象的东洞庭湖湿地植被信息提取技术[J].西北林学院学报,2013,28(4):170-175.[Qiao T, Zhang H Q, Chen Y F, et al. Extraction of vegetation information based on NDVI segmentation and object oriented Method[J]. Journal of Northwest Forestry College, 2013,28(4):170-175.]
[15] Gonzalez-Audieana M, Saleta J L, Catalan R G, et al. Fusion of multispectral and Panchromatic images using improved HIS and PCA mergers based on wavelet decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(6):1291-1299.
[16]何敏,张文君,王卫红.面向对象的最优分割尺度计算模型[J].大地测量与地球动力学,2009,29(1):106-109.[He M, Zhang W J, Wang W H. Optimal segmentation scale model based on object-oriented analysis method[J]. Geodetic measurement and geodynamics, 2009,29(1):106-109.]
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[19]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):194-196.[Liu Y,Du P J, Zheng H, et al. Classification of China small satellite remote sensing image based on random forests[J]. Science of Surveying and Mapping, 2012,37(4):194-196.]
[20] Verikas A, Gelzinis A, Bacauskiene M. Mining data with random forests:A survey and results of new tests[J]. Pattern Recognition, 2011,44(2):330-349.
[21] Belgiu M, Dragut L. Random forest in remote sensing:A review of applications and future directions[J]. ISPRS Journal of Photngrammetry and Remote Sensing, 2016,114:24-31.
[22]严婷婷,边红枫,廖桂项,等.森林湿地遥感信息提取方法研究现状[J].国土资源遥感,2014,26(2):11-18.[Yan T T,Bian H F, Liao G X, et al. Research status of methods for mapping forested wetlands based on remote sensing[J].Remote Sensing for Land&Resourse, 2014,26(2):11-18.]
[23]王书玉,张羽威,于振华.基于随机森林的洪河湿地遥感影像分类研究[J].测绘与空间地理信息,2014,37(4):83-85,93.[Wang S Y, Zhang Y W, Yu Z H. Classification of Honghe wetland remote sensing image based on random forests[J]. Geomatics&Spatial Information Technology,2014,37(4):83-85,93.]
[24] Dronova I. Object-based image analysis in wetland research:A review[J]. Remote Sensing, 2015,7(5):6380-6413.
[25]刘舒,姜琦刚,马玥,等.基于多目标遗传随机森林特征选择的面向对象湿地分类[J].农业机械学报,2017,48(1):119-127.[Liu S, Jiang Q G, Ma Y, et al. Object-oriented wetland classification based on hybrid feature selection method combining with relief multi-objective genetic algorithmand random forest[J]. Transactions of the Chinese Socierty for Agricultural Machinery, 2017,48(1):119-127.]
[26]刘家福,李林峰,任春颖,等.基于特征优选的随机森林模型的黄河口滨海湿地信息提取研究[J].湿地科学,2018,16(2):97-105.[Liu J F, Li L F, Ren C Y, et al. Information extraction of coastal wetlands in Yellow River Estuary by optimal feature-based random forest model[J].Wetland Science, 2018,16(2):97-105.]
[27]李方方,刘正军,徐强强,任海成.面向对象随机森林方法在湿地植被分类的应用[J].遥感信息,2018,33(1):111-116.[Li F F, Liu Z J, Xu Q Q, et al. Applicationof object-oriented random forest method in wetland vegetation classification[J]. Remote Sensing Information, 2018,33(1):111-116.]
[28] Masoud M, Bahram S, Fariba M, et al. An assessment of simulated compact polarimetric SAR data for wetland classification using random forest algorithm[C]. Canadian Journal of Remote Sensing, 2017,43:5,468-484.
[29]高分辨率对地观测系统吉林数据与应用中心. GF-2影像数据是2016年7月29日采集[DB/DK].[2016-7-29].[High Resolution Earth Observation System Jilin Data and Application Center. GF-2 image data was collected on July 29, 2016.[DB/DK].[2016-7-29].]
[30] Saucier A, Muller J. Using principal component analysis to enhance the generalized multifractal analysis approach to textural segmentation:Theory and application to microresistivity well logs[J]. Physica A:Statistical Mechanics and its Applications, 2002,309(3-4):419-444.
[31] Chabrier S, Emile B, Rosenberger C, et al. Unsupervised performance evaluation of image segmentation[J]. EURASIP Journal on Applied Signal Processing, 2006(1):096306.
[32] Breiman L. Random forests[J]. Machine Learning, 2001,45(1):5-32.
[2] Thakur J K, Srivastava P K, Singh S K, et al. Ecological monitwring of wetlands in semiarid region of Konya closed Basin Turkey[J]. Regional Environmental Change, 2012,12(1):133-144.
[3] Davranche A, Lefebvre G, Poulin B. Wetland monitoring using classification trees and SPORT-5 seasonal time series[J]. Remote Sensing Environment, 2010,114(3):552-562.
[4] Hong S, Jang H, Kim N, Sohn H. Water area extraction using RADARSAT SAR imagery combined with Landsat imagery and terrain information[J]. Sensors, 2015,15(3):6652-6667.
[5] Chen Y, He X, Wang J. Classification of coastal wetlands in eastern China using polarimetric SAR data[J]. Arabian Journal of Geosciences, 2015,8(12):10203-10211.
[6] Han X, Chen X, Feng L. Four decades of winter wetland changes in Poyang Lake based on Landsat observations between 1973 and 2013[J]. Remote Sensing Environment, 2015,156:426-437.
[7] Zhang S, Zhang C, Zhang L, et al. Wetland remote sensing classification using support vector machine optimized with genetic algorithm:A case study in Honghe Nature National Reserve[J]. Scientia Geograpgica Sinica, 2012,32(4):435-441.
[8] Liu L, Zang S, Na X, et al. Wetland mapping in Zhalong Natural Reserve using optical and radar remotely sensed data and ancillary topographical data[J]. Geographic Geoinformation Science, 2013,29(1):36-40.
[9] Laba M, Blair B, Downs R, et al. Use of textural measurements to map invasive wetland plants in the Hudson River National Estuarine Research Reserve with IKONOS satellite imagery[J]. Remote Sens. Environ, 2010,114(4):876-886.
[10] Dronova I, Gong P, Wang L. Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China[J]. Remote Sensing Environment,2011,115(12):3220-3236.
[11] Blaschke T, Hay G J, Kelly M, et al. Geographic object-based image analysis-towards a new paradigm[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014,87(100):180-191.
[12] Dronova I, Gong P, Wang L, et al. Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification[J]. Remote Sensing Environment, 2015,158:193-206.
[13] Blaschke T.Object-based image analysis for remote sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010,65(1):2-16.
[14]乔婷,张怀清,陈永富,等.基于NDVI分割与面向对象的东洞庭湖湿地植被信息提取技术[J].西北林学院学报,2013,28(4):170-175.[Qiao T, Zhang H Q, Chen Y F, et al. Extraction of vegetation information based on NDVI segmentation and object oriented Method[J]. Journal of Northwest Forestry College, 2013,28(4):170-175.]
[15] Gonzalez-Audieana M, Saleta J L, Catalan R G, et al. Fusion of multispectral and Panchromatic images using improved HIS and PCA mergers based on wavelet decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(6):1291-1299.
[16]何敏,张文君,王卫红.面向对象的最优分割尺度计算模型[J].大地测量与地球动力学,2009,29(1):106-109.[He M, Zhang W J, Wang W H. Optimal segmentation scale model based on object-oriented analysis method[J]. Geodetic measurement and geodynamics, 2009,29(1):106-109.]
[17]刘兆祎,李鑫慧,沈润平,等.高分辨率遥感图像分割的最优尺度选择[J].计算机工程与应用,2014,50(6):144-147.[Liu Z Y, Li X H, Shen R P, et al. Selection of the best segmentation scale in high-resolution image segmentation[J]. Computer Engineering and Applications, 2014,50(6):144-147.]
[18]殷瑞娟,施润和,李镜尧.一种高分辨率遥感影像的最优分割尺度自动选取方法[J].地球信息科学学报,2013,15(6):902-910.[Yin R J, Shi R H, Li J Y. Automatic selection of optimal segmentation scale of high-resolution remote sensing images[J]. Journal of Geo-information Science, 2013,15(6):902-910.]
[19]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):194-196.[Liu Y,Du P J, Zheng H, et al. Classification of China small satellite remote sensing image based on random forests[J]. Science of Surveying and Mapping, 2012,37(4):194-196.]
[20] Verikas A, Gelzinis A, Bacauskiene M. Mining data with random forests:A survey and results of new tests[J]. Pattern Recognition, 2011,44(2):330-349.
[21] Belgiu M, Dragut L. Random forest in remote sensing:A review of applications and future directions[J]. ISPRS Journal of Photngrammetry and Remote Sensing, 2016,114:24-31.
[22]严婷婷,边红枫,廖桂项,等.森林湿地遥感信息提取方法研究现状[J].国土资源遥感,2014,26(2):11-18.[Yan T T,Bian H F, Liao G X, et al. Research status of methods for mapping forested wetlands based on remote sensing[J].Remote Sensing for Land&Resourse, 2014,26(2):11-18.]
[23]王书玉,张羽威,于振华.基于随机森林的洪河湿地遥感影像分类研究[J].测绘与空间地理信息,2014,37(4):83-85,93.[Wang S Y, Zhang Y W, Yu Z H. Classification of Honghe wetland remote sensing image based on random forests[J]. Geomatics&Spatial Information Technology,2014,37(4):83-85,93.]
[24] Dronova I. Object-based image analysis in wetland research:A review[J]. Remote Sensing, 2015,7(5):6380-6413.
[25]刘舒,姜琦刚,马玥,等.基于多目标遗传随机森林特征选择的面向对象湿地分类[J].农业机械学报,2017,48(1):119-127.[Liu S, Jiang Q G, Ma Y, et al. Object-oriented wetland classification based on hybrid feature selection method combining with relief multi-objective genetic algorithmand random forest[J]. Transactions of the Chinese Socierty for Agricultural Machinery, 2017,48(1):119-127.]
[26]刘家福,李林峰,任春颖,等.基于特征优选的随机森林模型的黄河口滨海湿地信息提取研究[J].湿地科学,2018,16(2):97-105.[Liu J F, Li L F, Ren C Y, et al. Information extraction of coastal wetlands in Yellow River Estuary by optimal feature-based random forest model[J].Wetland Science, 2018,16(2):97-105.]
[27]李方方,刘正军,徐强强,任海成.面向对象随机森林方法在湿地植被分类的应用[J].遥感信息,2018,33(1):111-116.[Li F F, Liu Z J, Xu Q Q, et al. Applicationof object-oriented random forest method in wetland vegetation classification[J]. Remote Sensing Information, 2018,33(1):111-116.]
[28] Masoud M, Bahram S, Fariba M, et al. An assessment of simulated compact polarimetric SAR data for wetland classification using random forest algorithm[C]. Canadian Journal of Remote Sensing, 2017,43:5,468-484.
[29]高分辨率对地观测系统吉林数据与应用中心. GF-2影像数据是2016年7月29日采集[DB/DK].[2016-7-29].[High Resolution Earth Observation System Jilin Data and Application Center. GF-2 image data was collected on July 29, 2016.[DB/DK].[2016-7-29].]
[30] Saucier A, Muller J. Using principal component analysis to enhance the generalized multifractal analysis approach to textural segmentation:Theory and application to microresistivity well logs[J]. Physica A:Statistical Mechanics and its Applications, 2002,309(3-4):419-444.
[31] Chabrier S, Emile B, Rosenberger C, et al. Unsupervised performance evaluation of image segmentation[J]. EURASIP Journal on Applied Signal Processing, 2006(1):096306.
[32] Breiman L. Random forests[J]. Machine Learning, 2001,45(1):5-32.