Classification of coal gangue pile vegetation based on UAV remote sensing

被引：0

作者：

Zhou T. ^{[1
]}

Hu Z. ^{[1
,2
]}

Ruan M. ^{[2
]}

Liu S. ^{[1
]}

Zhang Y. ^{[3
]}

机构：

[1] School of Environmental and Spatial Informatics, China University of Mining and Technology, Xuzhou

[2] Institute of Land Reclamation and Ecological Restoration, China University of Mining and Technology-Beijing, Beijing

[3] School of Public & Management, China University of Mining and Technology, Xuzhou

来源：

Meitan Kexue Jishu/Coal Science and Technology (Peking) | 2023年 / 51卷 / 05期

关键词：

coal gangue pile; color space conversion; multi-feature priority selection; texture filtering; UAV remote sensing; vegetation classification;

D O I：

10.13199/j.cnki.cst.2021-0899

中图分类号：

学科分类号：

摘要：

The accurate classification of vegetation species is the basis for the evaluation of vegetation restoration effect of coal gangue pile. In this paper, the visible image of coal gangue pile in different seasons was obtained by UAV remote sensing technology. The color space conversion and texture filtering were used to adequately explore the rich features of color, structure and texture in the visible image. Then, the traditional artificial feature selection method was improved, which could quickly, simply and efficiently screen features information to obtain the optimal classification features, and the optimized results were fused with RGB images to obtain multi-feature fusion images. Finally, based on two stages of RGB images and multi-feature fusion images, the vegetation of coal gangue pile was classified by three supervised classification methods, including support vector machine (SVM), maximum likelihood (ML) and neural network (NN). Meanwhile, the accuracy of classification results was evaluated by confusion matrix and the dynamic changes of vegetation were analyzed. The results showed that the improved artificial feature selection method could screen out the optimal classification features of coal gangue pile vegetation in different seasons. The selected classification features can not only effectively reflect the differences of various ground features, but also reduce the redundancy of feature information to improve the accuracy and efficiency of image classification. The classification result based on Support Vector Machine Classification (SVM) combined with multi-feature fusion image had highest classification accuracy, and the overall classification accuracy could reach 90.60%, and the corresponding Kappa coefficient is 0.878 0, which was 9.74% and 0.126 5 higher than that of RGB image of the same period, respectively. And, the accuracy of MLC and NNC classification methods was less improved. Compared with the RGB images of the same period, the overall classification accuracy could be improved by 6.95% and 3.93%, respectively, and the corresponding Kappa coefficient could be improved by 0.084 5 and 0.054 1, respectively. At the same time, based on the result of optimal classification, this paper evaluated the vegetation restoration effect of coal gangue pile in Changcun from the perspectives of vegetation coverage and vegetation allocation pattern. The results showed that a variety of different vegetation allocation patterns were adopted by the coal gangue pile, and the vegetation coverage in autumn and summer is higher than 75%. The overall effect of vegetation restoration was better. This study could provide reference for the identification and classification of coal gangue piles vegetation information based on UAV visible light image, and meanwhile provide opinions or suggestions for the later management and maintenance of coal gangue piles vegetation restoration. © 2023 China Coal Society. All Rights Reserved.

引用

页码：245 / 259

页数：14

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