OBH-RSI:Object-Based Hierarchical Classification Using Remote Sensing Indices for Coastal Wetland

作     者：Zhaoyang Lin Jianbu Wang Wei Li Xiangyang Jiang Wenbo Zhu Yuanqing Ma Andong Wang 

作者机构：Beijing Key Laboratory of Fractional Signals and SystemsBeijing Institute of TechnologyBeijing 100081China Laboratory of Marine Physics and Remote SensingFirst Institute of OceanographyMinistry of Natural ResourcesQingdao 266061China Shandong Provincial Key Laboratory of Restoration for Marine EcologyShandong Marine Resources and Environment Research InstituteYantai 264006China Shandong Yellow River Delta National Nature Reserve Administration CommitteeDongying 257091China 

出 版 物：《Journal of Beijing Institute of Technology》 (北京理工大学学报（英文版）)

年 卷 期：2021年第30卷第2期

页      面：159-171页

核心收录：

学科分类：0810[工学-信息与通信工程] 083002[工学-环境工程] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 07[理学] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0824[工学-船舶与海洋工程] 081002[工学-信号与信息处理] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0713[理学-生态学] 

基　　金：supported by the Beijing Natural Science Foundation(No.JQ20021) the National Natural Science Foundation of China(Nos.61922013,61421001 and U1833203) the Remote Sensing Monitoring Project of Geographical Elements in Shandong Yellow River Delta National Nature Reserve 

主　　题：Yellow River Delta vegetation index object-based hierarchical classification wetland multi-source remote sensing 

摘      要：With the deterioration of the environment,it is imperative to protect coastal *** multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective *** object-based hierarchical classification using remote sensing indices(OBH-RSI)for coastal wetland is proposed to achieve fine classification of coastal ***,the original categories are divided into four groups according to the category ***,the training and test maps of each group are extracted according to the remote sensing ***,four groups are passed through the classifier in ***,the results of the four groups are combined to get the final classification result *** experimental results demonstrate that the overall accuracy,average accuracy and kappa coefficient of the proposed strategy are over 94%using the Yellow River Delta dataset.