面向对象分类方法在水土保持措施提取中的应用  

Application of object-oriented classification method in the extraction of soil and water conservation measures

作　　者：赵搏华 王秀茹[1] 阎世煜 张羽飞 张婷 ZHAO Bohua;WANG Xiuru;YAN Shiyu;ZHANG Yufei;ZHANG Ting(School of Soil and Water Conservation, Beijing Forestry University, 100083, Beijing, China;Dalian Institute of Science and Technology, 116052, Dalian, Liaoning, China)

机构地区：[1]北京林业大学水土保持学院,北京100083 [2]大连科技学院,辽宁大连116052

出　　处：《中国水土保持科学》2022年第1期122-127,共6页Science of Soil and Water Conservation

基　　金：水利部公益性行业科研专项“东北灌区节水灌溉生态效益评估”(201401001)。

摘　　要：无人机遥感影像与面向对象分类方法相结合的方式在水土保持监测中的应用越来越广泛,而不同的分类方法存在精度差异。为提高水土保持监测中措施分类测算的准确率,依托北京2022年冬奥会延庆赛区雪车雪橇中心水土保持监测工程,采用隶属度函数、最邻近分类法、支持向量机(SVM)、决策树以及随机森林5种分类方法,详细分析水土保持监测范围内2个片区的措施提取的精度差异。结果表明:1)5种分类方法的Kappa系数均>0.69,分类效果较好;其中,2个片区整体分类精度较好的是SVM分类法。2)挡墙适用最邻近分类法,精度为71.42%。3)SVM分类法对植被和第一片区的临时苫盖措施(裸土)分类精度较好,精度分别为93.25%和80.0%;对编织袋装表土和排水沟的分类精度和为81.51%和70.34%。最邻近分类法对第二片区的临时苫盖措施(裸土)、临时苫盖措施(植物)、框格护坡的精度较好,精度分别为73.94%、76.23%、66.37%。综上所述,SVM分类法更适用本研究的水土保持措施分类。[Background]The combination of UAV remote sensing images and object-oriented classification methods is more and more widely used in soil and water conservation monitoring.This method can improve the efficiency and accuracy of the calculation after classification of the ground features in the project area.However,different object-oriented classification methods have different extraction accuracy for different ground features.Based on the water and soil conservation monitoring project of the Snowmobile Sled Center in the Yanqing Competition Area of the Beijing 2022 Winter Olympics,this study analyzed the accuracy difference of measure extraction in two areas within the monitoring range of soil and water conservation.[Methods]Based on UAV remote sensing image data and object-oriented classification methods,5 classification methods,including membership function,the nearest neighbor classification,support vector machine(SVM),classification and regression tree(Cart)and random forest(RF)were adapted to extract the parameters of soil and water conservation measures.Three indicators,namely overall accuracy,Kappa coefficient,and producer accuracy(PA)were adapted to quantitatively evaluate the classification results by the five classification methods.Among them,the overall accuracy and Kappa coefficient were used to compare the overall classification accuracy,and PA was used to evaluate the classification accuracy of the specific land class.[Results]1)The Kappa coefficients by 5 classification methods were all above 0.69,indicating that the classification effect was good.Among them,the overall classification accuracy of the two sections was better by SVM classification method.2)The retaining wall was applicable to the nearest neighbor classification method with an accuracy of 71.42%.3)The classification accuracy of SVM for vegetation and temporary coverage measures(bare soil)in the first area was better,and the PA was 93.25%and 80.0%.The better classification accuracy of weaving bags of surface soil and drainage ditch was a

关 键 词：水土保持监测 面向对象分类 水土保持措施 无人机遥感影像 冬奥会延庆赛区 

分 类 号：S157[农业科学—土壤学]