基于不同分类器的农用地分类提取
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摘要
【目的】分析Landsat 8 OLI卫星遥感影像数据面向农用地分类的实际应用方法和效果,以新疆奇台县南部为研究对象。【方法】使用随机森林(RF)、支持向量机(SVM)和神经网络(Neural Net)三种分类器进行研究区农用地分类对比。【结果】通过对三种分类器参数设置参数精度检验,利用上述三种算法对农用地地物分类进行精度评价,在整体分类精度中,支持向量机算法(SVM)<随机森林算法(RF)<神经网络算法(Neural Net),分类精度分别为:90.75%,94.30%和94.84%。【结论】神经网络方法(Neural Net)在该地区的农用地物整体分类上,比支持向量机(SVM)和随机森林法(RF)相比具有一定的优势,并获得较好的分类精度。
【Objective】 Classification based on remote sensing image is one of the important contents of remote sensing data application. How to improve the classification accuracy of remote sensing image is the key of remote sensing image research. 【Method】In order to analyze the practical application method and effect of Landsat 8 OLI satellite remote sensing image data for agricultural land classification, this paper takes the southern Qitai County of Xinjiang as the research object, and uses three classifiers, random forest(RF), support vector machine(SVM) and neural network(Neural Net), to conduct a comparative study of agricultural land classification in the study area. 【Result】Through the parameter setting accuracy test of the three classifiers, the accuracy of agricultural land classification is evaluated by using the above three algorithms. In the overall classification accuracy, the support vector machine algorithm(SVM) < random forest algorithm(RF) < neural network algorithm(Neural Net) has the classification accuracy of 90.75%, 94.30% and 94.84%, respectively. 【Conclusion】Neural Net has some advantages over Support Vector Machine(SVM) and Random Forest Method(RF) in the overall classification of agricultural land use in this area, and achieves better classification accuracy.
【Objective】 Classification based on remote sensing image is one of the important contents of remote sensing data application. How to improve the classification accuracy of remote sensing image is the key of remote sensing image research. 【Method】In order to analyze the practical application method and effect of Landsat 8 OLI satellite remote sensing image data for agricultural land classification, this paper takes the southern Qitai County of Xinjiang as the research object, and uses three classifiers, random forest(RF), support vector machine(SVM) and neural network(Neural Net), to conduct a comparative study of agricultural land classification in the study area. 【Result】Through the parameter setting accuracy test of the three classifiers, the accuracy of agricultural land classification is evaluated by using the above three algorithms. In the overall classification accuracy, the support vector machine algorithm(SVM) < random forest algorithm(RF) < neural network algorithm(Neural Net) has the classification accuracy of 90.75%, 94.30% and 94.84%, respectively. 【Conclusion】Neural Net has some advantages over Support Vector Machine(SVM) and Random Forest Method(RF) in the overall classification of agricultural land use in this area, and achieves better classification accuracy.
引文
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[15]张锦水,何春阳 ,潘耀忠 ,等.基于SVM的多源信息复合的高空间分辨率遥感数据分类研究 [J].2006,10(1):49-57.ZHANG Jin-shui,HE Chun-yang,PAN Yao-zhong,et al.(2006).The High Spatial Resolution RS Image Classification Based on SVM Method with the Multi-Source Data[J].Journal of Remote Sensing Journal of Remote Sensing.10(1):49-57.(in Chinese)
[16]冯恒栋.基于人工神经网络和模糊分类的森林植被遥感图像分类研究[D].沈阳,东北师范大学,2009.FENG Heng-dong.(2009).Classification of Forest Vegetation Remote Sensing Images Based on Artificial Neural Network and Fuzzy Classification [D].Master Disseration.Shen yang normal university,Shenyang.(in Chinese)
[17] 陈祎荻,秦玉平.基于机器学习的文本分类方法综述[J],渤海大学学报(自然科学版),2010,31(2):201-205.CHEN Yi-di,QIN Yu-ping.(2010).Review on text categorization methods based on machine learning[J].Journal of Bohai University(Natural Science Edition),31(2):201-205.(in Chinese)
[18] 都业军,周肃,方吉宙,等.人工神经网络在遥感影像分类中的应用与对比研究[J],测绘科学,2010,(S1),120-121,51.DU Ye-Jun,ZHOU Su,FANG Ji-zhou,et al.(2010).Application and Contrast Research on Remote Sensing Image Classification Based on Artificial Neural Network[J],Science of Surveying and Mapping,(S1),120-121,51.(in Chinese)
[19] 闫琰,董秀兰,李燕.基于ENVI的遥感图像监督分类方法比较研究[J].北京测绘,2011,(3):14-16.YAN Yan,DONG Xiu-lan,LI Yan.(2011).The Comparative Study of Remote Sensing Image Supervised Classification Methods Based on ENVI[J] Beijing Surveying and Mapping,(3):14-16.(in Chinese)
[2] 郭航,张晓丽.基于遥感技术的植被分类研究现状与发展趋势[J],2007,20(3):14-19.GUO Hang,ZHANG Xiao-li.(2007).Current Status and Developing Trend in Vegetation Classification Based on RS Technology[J].WORLD FORESTRY RESEARCH ,20(3):14-19.(in Chinese)
[3]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],2012,37(4):194-196.LIU Yi,DU Pei-jun,ZHENG Hui,et al .(2012).Classification of China small satellite remote sensing image based on random forests[J].Science of Surveying and Mapping,37(4):194-196.(in Chinese)
[4] 张锐.基于改进支持向量机的盐碱地信息精确提取方法研究[J].灌溉排水学报,2018,37(9):62-68.ZHANG Rui.(2018).An Improved Support Vector Machine Method for Estimating Saline-alkali Soil from Remote Sensing Imagery [J].Journal of Irrigation and Drainage,37(9):62-68.(in Chinese)
[5]徐浩田.基于支持向量机的湿地遥感分类及生态系统健康评价研究[D].沈阳:沈阳农业大学硕士论文,2017.XU Hao-tian.(2014).Study on the wetland remote sensing classification and ecosystem health evaluation based on support vector machine [D].Master Disseration.Shen-yang agricultural university,Shenyang.(in Chinese)
[6]张浩,赵云胜,陈冠宇,等.基于支持向量机的遥感图像建筑物识别与分类方法研究[J].地质科技情报,2016,35(6):18-24.ZHANG Hao,ZHAO Yun-sheng,CHEN Guan-yu ,et al.(2016).Remote Sensing Image Building Recognition and Classification Based on the Support Vector Machine[J].Geological Science and Technology Inforamtion,35(6):18-24.(in Chinese)
[7]陈芳.基于支持向量机的高分辨率遥感影像的舰船目标识别研究[J].船舶科学技术,2017,39(12A):180-183.CHEN Fang.(2017).Research on Ship Target Recognition Based on High Resolution Remote Sensing Image of Support Vector Machine [J].Ship Science and Technology,39(12A):180-183.(in Chinese)
[8]程彬.基于支持向量机的乾安县土地利用遥感分类研究[J].长春师范大学学报,2017,36(6):86-88.CHENG Bin.(2017).Land Use Information Extraction Based on Support Vector Machine Using Multitemporal Remote Sensing in Qian'an County[J].Journal of Changchun Normal University(Natural Science),36(6):86-88.(in Chinese)
[9]Rodriguez-Galiano,V.F,Ghimire ,B,Rogan,J.et al.(2012).An assessment of the effectiveness of a random forest classifier for land-cover classification[J].ISPRS Journal of Photogrammetry and Remote Sensing,67:93-104.
[10]Miao,X,Heaton,J,S,Zheng,S,et al.(2012 ).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,33(6):1,823-1,849.
[11]Clark,M.L,Roberts,D.A.(2012) .Species-level differences in hyperspectral metrics among tropical rainforest tress as determined by a tree-based classifier[J].Remote Sensing,4(6):1,820-1,855.
[12]Pierce,A .D,Farris,C.A ,Taylor,A .H.(2012).Use of random forests for modeling and mapping forest canopy fuels for fire behavior analysis in Lassen Volcanic National Park,California USA[J].Forest Ecology and management,279:77-89.
[13]李垒,任越美.基于随机森林的高光谱遥感图像分类[J].计算机应用研究,2016,52(54):189-195.LI Lei,REN Yue-mei.(2016).Classification of hyperspectral data based on random forest[J].Computer Engineering and Applications,52(54):189-195.(in Chinese)
[14]郭玉宝,池天河,彭玲,等.利用随机森林的高分一号遥感数据进行城市用地分类[J].测绘通报,2016,(5):73-76.GUO Yu-bao,CHI Tian-he,PENG Ling,et al.(2016).Classification of GF-1 Remote Sensing Image Based on Random Forests for Urban Land-use[J].Bulletin of Surveying and Mapping,(5):73-76.(in Chinese)
[15]张锦水,何春阳 ,潘耀忠 ,等.基于SVM的多源信息复合的高空间分辨率遥感数据分类研究 [J].2006,10(1):49-57.ZHANG Jin-shui,HE Chun-yang,PAN Yao-zhong,et al.(2006).The High Spatial Resolution RS Image Classification Based on SVM Method with the Multi-Source Data[J].Journal of Remote Sensing Journal of Remote Sensing.10(1):49-57.(in Chinese)
[16]冯恒栋.基于人工神经网络和模糊分类的森林植被遥感图像分类研究[D].沈阳,东北师范大学,2009.FENG Heng-dong.(2009).Classification of Forest Vegetation Remote Sensing Images Based on Artificial Neural Network and Fuzzy Classification [D].Master Disseration.Shen yang normal university,Shenyang.(in Chinese)
[17] 陈祎荻,秦玉平.基于机器学习的文本分类方法综述[J],渤海大学学报(自然科学版),2010,31(2):201-205.CHEN Yi-di,QIN Yu-ping.(2010).Review on text categorization methods based on machine learning[J].Journal of Bohai University(Natural Science Edition),31(2):201-205.(in Chinese)
[18] 都业军,周肃,方吉宙,等.人工神经网络在遥感影像分类中的应用与对比研究[J],测绘科学,2010,(S1),120-121,51.DU Ye-Jun,ZHOU Su,FANG Ji-zhou,et al.(2010).Application and Contrast Research on Remote Sensing Image Classification Based on Artificial Neural Network[J],Science of Surveying and Mapping,(S1),120-121,51.(in Chinese)
[19] 闫琰,董秀兰,李燕.基于ENVI的遥感图像监督分类方法比较研究[J].北京测绘,2011,(3):14-16.YAN Yan,DONG Xiu-lan,LI Yan.(2011).The Comparative Study of Remote Sensing Image Supervised Classification Methods Based on ENVI[J] Beijing Surveying and Mapping,(3):14-16.(in Chinese)
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