Classification results based on the feature combinations of study area B.

<p>(a) RGB& selected VIs; (b) RGB& selected RGB texture; (c) RGB& selected HSV texture; (d) RGB& selected VIs &RGB texture & HSV texture; (e) RGB& selected features by RF1; (f) RGB& selected features by RF2.</p>

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Bibliographic Details
Main Author:	Junliang Dong (21300792) (author)
Other Authors:	Jian Zhang (1682) (author), Suo Zhang (12041123) (author), Zhiyong Yu (782893) (author), Ziheng Song (14380161) (author), Tianya Meng (21300795) (author)
Published:	2025
Subjects:	Biochemistry Ecology Inorganic Chemistry Plant Biology Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified unmanned aerial vehicle highly heterogeneous environments brings great challenges accurate ecological assessments selected features yielded selected features based mining ecological monitoring mining ecological assessments extract vegetation indexes superior optimal combination feature selection method study area b fine vegetation mapping uav rgb imagery mining area study area vegetation mapping mining areas feature combinations adopted method study utilized texture features vegetation identification vegetation extraction vegetation classification svm ). standard deviation single type rgb images rgb image results demonstrated highest accuracy field crops effectively identify different combinations difference coefficient conducting pre best combination
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Classification results based on the feature combinations of study area B.

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