Assessing machine-learning algorithms and image- and lidar-derived variables for GEOBIA classification of mining and mine reclamation-Reference-Cited by-同舟云学术

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Assessing machine-learning algorithms and image- and lidar-derived variables for GEOBIA classification of mining and mine reclamation

Published:2015-02-16 Issue:4 Volume:36 Page:954-978
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Maxwell A.E.¹,Warner T.A.²,Strager M.P.³,Conley J.F.²,Sharp A.L.¹

Affiliation:

1. College of Science, Technology, and Mathematics, Alderson Broaddus University, Philippi, WV, USA

2. Geology and Geography Department, West Virginia University, Morgantown, WV, USA

3. Division of Resource Management, West Virginia University, Morgantown, WV, USA

Funder

U.S. Geological Survey

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2014.1001086

Reference107 articles.

1. An Introduction to Categorical Data Analysis

2. Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses

3. Remote sensing of selective logging in Amazonia

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