Machine learning in geosciences and remote sensing

• Published in: Di xue qian yuan. Vol. 7; no. 1; pp. 3 - 10
• Main Authors: Lary, David J., Alavi, Amir H., Gandomi, Amir H., Walker, Annette L.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2016; Hanson Center for Space Science, University of Texas at Dal as, Richardson, TX 75080, USA%Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA%BEAC0N Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA%Aerosol and Radiation Section, Naval Research Laboratory, 7 Grace Hopper Ave., Stop 2, Monterey, CA 93943-5502, USA
• Subjects: Classification; Geosciences; Machine learning; Regression; Remote sensing; 地球科学; 基于案例的推理; 应用程序; 应用软件; 机器学习; 遗传编程; 遥感; 非参数回归; Regression; Geosciences; Remote sensing; Machine learning; Classification
• ISSN: 1674-9871
• DOI: 10.1016/j.gsf.2015.07.003

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.