基于规则验证点的面向对象分类精度评价  

Accuracy Assessment of Object-Oriented Classification Based on Regular Verification Points

作　　者：龚循强[1,2] 刘星雷 鲁铁定 刘丹[2] Gong Xunqiang;Liu Xinglei;Lu Tieding;Liu Dan(Fundamental Science on Radioactive Geology and Exploration Technology Laboratory,East China University of Technology,Nanchang,Jiangxi 330013,China;Faculty of Geomatics,East China University of Technology,Nanchang,Jiangxi 330013,China)

机构地区：[1]东华理工大学放射性地质与勘探技术国防重点学科实验室,江西南昌330013 [2]东华理工大学测绘工程学院,江西南昌330013

出　　处：《激光与光电子学进展》2020年第24期281-286,共6页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(41701437);江西省高等学校教学改革研究省级课题(JXJG-19-6-13);江西省数字国土重点实验室开放基金(DLLJ201805);东华理工大学放射性地质与勘探技术国防重点学科实验室开放基金(RGET1905)。

摘　　要：遥感图像分类是图像分析的重要步骤,其中分类后精度评定是判定图像分类效果的主要依据。目前,面向对象分类的精度评定常采用随机验证点作为评定参数,这样容易造成评定的分类结果精度不高。提出基于规则验证点的面向对象的分类精度评价方法,在使用支持向量机、CART(classification and regression tree)决策树和K最近邻进行分类的基础上,分别采用基于规则验证点和随机验证点的方法对分类结果进行精度评定。实验结果表明,所提出的方法比传统的基于随机验证点的方法得到的分类精度更高。三种分类方法在规则验证点下的最优总体分类精度分别达到了87.92%、91.94%和94.63%,均优于基于随机验证点的方法的精度评定结果。Remote sensing image classification is an important part of image analysis,and post-classification accuracy assessment is the main basis for determining the effect of image classification.At present,random verification points are often used as assessment parameters in object-oriented classification,which may easily lead to inaccurate classification results.An object-oriented classification accuracy assessment method based on regular verification points is proposed in this paper.Regular and random verification points are used to evaluate the classification accuracy by using support vector machine,CART(classification and regression tree)decision tree,and K nearest neighbor classification.Experimental results show that the proposed method has higher classification accuracy than traditional methods based on random verification points.The optimal overall classification accuracy of the three classification methods based on the regular verification points reaches 87.92%,91.94%,and 94.63%,respectively,which are better than the accuracy assessment results of random verification points based methods.

关 键 词：测量 遥感图像 面向对象分类 规则验证点 精度评定 K最近邻 

分 类 号：O433.4[机械工程—光学工程]