Machine learning in geosciences and remote sensing

作     者：David J.Lary Amir H.Alavi Amir H.Gandomi Annette L.Walker 

作者机构：Hanson Center for Space ScienceUniversity of Texas at Dallas Department of Civil and Environmental EngineeringMichigan State University BEACON Center for the Study of Evolution in ActionMichigan State University Aerosol and Radiation SectionNaval Research Laboratory 

出 版 物：《Geoscience Frontiers》 (地学前缘（英文版）)

年 卷 期：2016年第7卷第1期

页      面：3-10页

核心收录：

学科分类：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）] 08[工学] 09[农学] 0804[工学-仪器科学与技术] 0903[农学-农业资源与环境] 0816[工学-测绘科学与技术] 081602[工学-摄影测量与遥感] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程] 

基　　金：Direct For Computer & Info Scie & Enginr Division Of Computer and Network Systems Funding Source: National Science Foundation 

主　　题：Machine learning Geosciences Remote sensing Regression Classification 

摘      要：Learning incorporates a broad range of complex procedures. Machine learning（ML） is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficultto-program applications, and software applications. It is a collection of a variety of algorithms（e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.） that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore,nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.