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Landscape tree species recognition using RedEdge-MX: Suitability analysis of two different texture extraction forms under MLC and RF supervision by Huaipeng Liu; Xiaoyan Su; Chuancai Zhang; Huijun An is a Earth and Planetary Sciences article available to read on EtoBox.

What is Landscape tree species recognition using RedEdge-MX: Suitability analysis of two different texture extraction forms under MLC and RF supervision about?

The size of the texture extraction window impacts image tree species classification, and the determination of the optimal texture extraction window requires the supervision of a specific classifier for accuracy. Therefore, it is necessary to analyse which kind of classifier is more suitable and should be to choose. In this study, we extracted eight types of textures, namely mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation, changed the window size by gradient increase and used maximum likelihood classification (MLC) and random forest (RF) to supervise and determine their optimal extraction windows, respectively. Finally, the optimised time consumption and classification accuracy for tree species classification was identified. The time consumption of MLC was significantly less than that of RF; however, neither was very long; for most textures, the optimal texture extraction window determined by MLC supervision was larger than that determined by RF supervision; in the classification of most feature sets, the overall accuracy obtained by MLC was less than that of RF. Because the time consumption of the texture extraction was much greater than

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Author

Huaipeng Liu; Xiaoyan Su; Chuancai Zhang; Huijun An

Publisher

Walter de Gruyter GmbH

Published

2022

Language

EN

Field

Earth and Planetary Sciences (Physical Sciences)