软硬结合分类方法提取特定地物信息——以冬小麦为例  

THE SPECIFIC LAND COVER EXTRACTION USING THE SOFT AND HARD CLASSIFICATION——AN EXAMPLE OF WINTER WHEAT

作　　者：朱爽[1,2] 张锦水[2,3] 李长青 郑阔[1] 占文峰[1] Zhu Shuang;Zhang Jinshui;Li Changqing;Zheng Kuo;Zhan Wenfeng(Beijing Polytechnic College,Beijing 100042,China;Institute of Remote Sensing and Engineering,Beijing Normal University,Beijing 100875,China;School of Geographical Science,Beijing Normal University,Beijing 100875,China)

机构地区：[1]北京工业职业技术学院,北京100042 [2]北京师范大学遥感科学与工程研究院,北京100875 [3]北京师范大学地理科学学部,北京100875

出　　处：《中国农业资源与区划》2020年第8期31-40,共10页Chinese Journal of Agricultural Resources and Regional Planning

基　　金：国家重点研发计划(2017YFD0300402-6),高分辨率对地观测系统重大专项(民用部分)(09-Y20A05-9001-17/18);北京市教育委员会科技计划一般项目(KM201810853006);北京工业职业技术学院一般课题(bgzyky 201916)。

摘　　要：[目的]针对影像上纯净、混合像元共存的现象,文章结合硬分类方法和软分类方法各自的优势,提出了目标地物信息的软硬结合的分类方法。[方法]该方法将遥感影像划分为典型目标地物像元、非目标地物像元和混合目标地物像元3个部分。典型的目标地物像元和非目标地物像元,采用硬分类方法(ISODATA)聚类确定类型;混合目标地物像元采用非线性支撑向量回归混合像元分解模型,从目标地物端元光谱库和非目标地物端元光谱库中多次随机选择像元,进行目标地物不同丰度值的混合像元模拟,构建样本库进行支撑向量回归,提取出混合像元的目标地物丰度。该文以冬小麦为研究对象,选用2006年4月7日的TM影像,采用软硬结合的分类方法进行冬小麦识别。[结果]较传统的硬、软分类方法,软硬结合分类方法精度高,总体精度达到了90.2%;而软分类方法为86.6%,硬分类方法为81.6%。[结论]软硬结合的分类方法克服了硬分类方法对混合像元信息提取受到光谱不确定影响,也克服了软分类方法受到光谱异质性干扰的问题。该分类方法简便、易操作,适合单目标特定地物的信息提取。Land use/cover mapping is one of the most widely used fields of remote sensing technology.At present,the methods of recognizing specific features from single-phase remote sensing images mainly include hard classification and soft classification.The accuracy of land use/cover classification is influenced by the spatial resolution and spectral characteristics of image.Wheat is one of the most important crops in China with the area of 1/5 of the whole crop area all over the country.Therefore,acquiring the information of crop acreage,especially wheat acreage timely and accurately,is important in making a method for regional economic development to guide the adjustment of planting structure and improving agricultural management.Land use/cover mapping is one of the most widely used fields of remote sensing technology.At present,the methods of recognizing specific features from single-phase remote sensing images mainly include Hard Classification method(HC)and Soft Classification method(SC).And there are also some shortcomings about these two methods.HC methods,such as maximum likelihood method or minimum distance method,classify each pixel as a ground object type,and the information results of each pixel are independent and irrelevant.Therefore,the hard classification is immune to the spectral fluctuation,which is not suitable for identifying mixed pixels.Soft classification methods,such as linear decomposition method,neural network method,supervised fuzzy classification method,support vector method,decision tree method,etc.,are applied to classify mixed pixel,and the extraction accuracy is higher than that of hard classification method.However,due to the characteristics of this method and the influence of spectral fluctuation,the accuracy of soft classification method is lower than that of hard classification method in the extraction of terrain objects in the pure area.To take advantage of conventional HC and SC,the soft and hard classification method(SHC)was proposed to express the variation of crop distribution by o

关 键 词：混合像元 软分类 硬分类 ISODATA 支撑向量回归 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置]