Developing a web-based system for supervised classification of remote sensing images

作     者：Sun, Ziheng Fang, Hui Di, Liping Yue, Peng Tan, Xicheng Bai, Yuqi 

作者机构：George Mason Univ CSISS 4400 Univ DrMSN 6E1 Fairfax VA 22030 USA Wuhan Univ LIESMARS Wuhan Hubei Peoples R China Wuhan Univ Int Sch Software Spatial Informat & Digital Technol Dept Wuhan Hubei Peoples R China Tsinghua Univ Ctr Earth Syst Sci Minist Educ Key Lab Earth Syst Modeling Beijing 100084 Peoples R China 

出 版 物：《GEOINFORMATICA》 (地学信息)

年 卷 期：2016年第20卷第4期

页      面：629-649页

核心收录：

学科分类：07[理学] 0705[理学-地理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：U.S. Department of Energy [DE-NA0001123] U.S. National Science Foundation [ICER-1440294] National Natural Science Foundation of China [91438203, 41271397, 51277167] Hubei Science and Technology Support Program [2014BAA087] ICER Directorate For Geosciences Funding Source: National Science Foundation 

主　　题：Supervised classification Geoprocessing web service Web-based processing system Remote sensing image Cyberinsfrastructure 

摘      要：Web-based image classification systems aim to provide users with an easy access to image classification function. The existing work mainly focuses on web-based unsupervised classification systems. This paper proposes a web-based supervised classification system framework which includes three modules: client, servlet and service. It comprehensively describes how to combine the procedures of supervised classification into the development of a web system. A series of methods are presented to realize the modules respectively. A prototype system of the framework is also implemented and a number of remote sensing (RS) images are tested on it. Experiment results show that the prototype is capable of accomplishing supervised classification of RS images on the Web. If appropriate algorithms and parameter values are used, the results of the web-based solution could be as accurate as the results of traditional desktop-based systems. This paper lays the foundation on both theoretical and practical aspects for the future development of operational web-based supervised classification systems.