面向对象分类方法可以充分利用遥感影像的光谱和空间信息,是一种适合于高分辨遥感影像的分类方法。以2012年资源3号卫星高分辨率遥感影像(ZY-3)为数据源,对基于面向对象与最大似然监督分类的地类信息提取方法进行了对比分析。面向对象分析方法中采用改进后的局部方差法确定并选取不同地类类型的最优分割尺度,并采用多尺度层次的方法提取不同地类类型信息。结果表明根据改进后的局部方差法确定的针叶林、阔叶林、针阔混交林地类类型的最优分割尺度为105;农田地类的最优分割尺度为105,水域、建筑类型的最优分割尺度为65。基于面向对象技术的地类信息提取方法其总体精度达到90.3%,Kappa系数为0.82;最大似然法其总体精度为77.6%,Kappa系数为0.71;基于面向对象方法的总体精度提高了12.7%,Kappa系数提高了11%。表明了基于面向对象分析方法的地类信息提取在国产高分辨率影像上的适用性。同时,论文的研究也为森林资源调查中地类信息的遥感提取进行了有益的尝试。
Object-oriented classification method which is suitable for the high resolution remote sensing images can make full use of the spectral and spatial information of remote sensing images.In this study,the information extraction method of object-oriented was compared to maximum likelihood method based on 2012 ZY-3 satellite high resolution remote sensing image.The results show that the optimal segmentation scale of coniferous and broad-leaves and coniferous and broad-leaves mixed forest is 105,water and building is 65 according to improved local variance method.In object-oriented image analysis,the optimal segmentation scale of different land types was selected by improved local variance method,the ground features of different types were extracted in multi-scale level.The accuracy of high resolution remote sensing image information extraction based on object-oriented image analysis technology was 90.3%,kappa coefficient is 0.82;the accuracy of high resolution remote sensing image information extraction based on maximum likelihood method was 77.6%,kappa coefficient is 0.71;the overall accuracy of the object-oriented image analysis technology is improved by12.7% and Kappa coefficient increases by 11%.It shows obviously that the object-oriented image analysis technology can be applied to domestic high resolution image information extraction.This paper also attempts to extract land use information by remote sensing images in the investigation of forest resources.
[1]Vander S C J,Jong S M,Roo A P J.A segmentation and classification approach of IKONOS-2 imagery for cover mapping to assist flood risk and flood damage assessment[J].International Journal of Applied Earth Observation and Geoinformation,2003(4):217-229.
[2]Huang C.Davis L S.Townshend J R G.An assessment of support vector machines for landcoverclassification[J].International Journal of Remote Sensing,2002,23(4):725-749.
[3]Volker W.Object-based classification of remote sensing data for change detection[J].ISPRS Journal of Photo-grammetry & Remote Sensing,2004(58):225-238.
[4]陈秋晓,骆剑承,周成虎.基于多特征的遥感影像分类方法[J].遥感学报,2004,8(3):239-245.
[5]Magnussen S.Boudewyn P.Wulder M contextual classification of Landsat TM images to forest inventory covertypes[J].International Journal of Remote Sensing,2004,25(12):2421-2440.
[6]许传阳,李建红.高分辨率遥感影像结合LiDAR数据的面向对象分类方法[J].河南理工大学学报:自然科学版,2015(2):82-85.
[7]马浩然,赵天忠,曾怡.面向对象的最优分割尺度下多层次森林植被分类[J].东北林业大学学报,2014(9):52-57.
[8]朱红春,蔡丽杰,刘海英,等.高分辨率影像分类的最优分割尺度计算[J].测绘科学,2015(3):71-75.
[9]丁晓英.eCognition在土地利用项目中的应用[J].测绘与空间地理信息系统,2005,28(6):116-120.
[10]孔令婷,杨英宝,章勇.基于不同分辨率遥感影像的分类方法对比研究[J].测绘与空间地理信息,2015(3):40-43.
[11]Mathieu R,Aryal J.Object-oriented classification and IKONOS multispectral imagery for mapping vegetation communities in urban areas[C]//17th Annual Colloquium of the Spatial Information Research Centre,Dunedin,New,Zealand:2005.
[12]王露,刘庆元.高分辨率遥感影像多尺度分割中最优尺度选取方法综述[J].测绘与空间地理信息,2015(3):166-169.
[13]周春艳,王萍,张振勇,等.基于面向对象信息提取技术的城市用地分类[J].遥感技术与应用,2008,23(1):31-36.
