Classification of coal gangue pile vegetation based on UAV remote sensing
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 filt...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tao ZHOU [verfasserIn] Zhenqi HU [verfasserIn] Mengying RUAN [verfasserIn] Shuguang LIU [verfasserIn] Yuhang ZHANG [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Chinesisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Meitan kexue jishu - Editorial Department of Coal Science and Technology, 2022, 51(2023), 5, Seite 245-259 |
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| Übergeordnetes Werk: |
volume:51 ; year:2023 ; number:5 ; pages:245-259 |
| Links: |
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| DOI / URN: |
10.13199/j.cnki.cst.2021-0899 |
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| Katalog-ID: |
DOAJ100976298 |
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| 520 | |a 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 0.8780, which was 9.74% and 0.1265 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 0.0845 and 0.0541, 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. | ||
| 650 | 4 | |a uav remote sensing | |
| 650 | 4 | |a coal gangue pile | |
| 650 | 4 | |a vegetation classification | |
| 650 | 4 | |a color space conversion | |
| 650 | 4 | |a texture filtering | |
| 650 | 4 | |a multi-feature priority selection | |
| 653 | 0 | |a Mining engineering. Metallurgy | |
| 700 | 0 | |a Zhenqi HU |e verfasserin |4 aut | |
| 700 | 0 | |a Mengying RUAN |e verfasserin |4 aut | |
| 700 | 0 | |a Shuguang LIU |e verfasserin |4 aut | |
| 700 | 0 | |a Yuhang ZHANG |e verfasserin |4 aut | |
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10.13199/j.cnki.cst.2021-0899 doi (DE-627)DOAJ100976298 (DE-599)DOAJe05c2f8fd8544a8e922e2439eae8516e DE-627 ger DE-627 rakwb chi TN1-997 Tao ZHOU verfasserin aut Classification of coal gangue pile vegetation based on UAV remote sensing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 0.8780, which was 9.74% and 0.1265 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 0.0845 and 0.0541, 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. uav remote sensing coal gangue pile vegetation classification color space conversion texture filtering multi-feature priority selection Mining engineering. Metallurgy Zhenqi HU verfasserin aut Mengying RUAN verfasserin aut Shuguang LIU verfasserin aut Yuhang ZHANG verfasserin aut In Meitan kexue jishu Editorial Department of Coal Science and Technology, 2022 51(2023), 5, Seite 245-259 (DE-627)588190470 (DE-600)2469839-8 02532336 nnns volume:51 year:2023 number:5 pages:245-259 https://doi.org/10.13199/j.cnki.cst.2021-0899 kostenfrei https://doaj.org/article/e05c2f8fd8544a8e922e2439eae8516e kostenfrei http://www.mtkxjs.com.cn/article/doi/10.13199/j.cnki.cst.2021-0899 kostenfrei https://doaj.org/toc/0253-2336 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_2055 AR 51 2023 5 245-259 |
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10.13199/j.cnki.cst.2021-0899 doi (DE-627)DOAJ100976298 (DE-599)DOAJe05c2f8fd8544a8e922e2439eae8516e DE-627 ger DE-627 rakwb chi TN1-997 Tao ZHOU verfasserin aut Classification of coal gangue pile vegetation based on UAV remote sensing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 0.8780, which was 9.74% and 0.1265 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 0.0845 and 0.0541, 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. uav remote sensing coal gangue pile vegetation classification color space conversion texture filtering multi-feature priority selection Mining engineering. Metallurgy Zhenqi HU verfasserin aut Mengying RUAN verfasserin aut Shuguang LIU verfasserin aut Yuhang ZHANG verfasserin aut In Meitan kexue jishu Editorial Department of Coal Science and Technology, 2022 51(2023), 5, Seite 245-259 (DE-627)588190470 (DE-600)2469839-8 02532336 nnns volume:51 year:2023 number:5 pages:245-259 https://doi.org/10.13199/j.cnki.cst.2021-0899 kostenfrei https://doaj.org/article/e05c2f8fd8544a8e922e2439eae8516e kostenfrei http://www.mtkxjs.com.cn/article/doi/10.13199/j.cnki.cst.2021-0899 kostenfrei https://doaj.org/toc/0253-2336 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_2055 AR 51 2023 5 245-259 |
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10.13199/j.cnki.cst.2021-0899 doi (DE-627)DOAJ100976298 (DE-599)DOAJe05c2f8fd8544a8e922e2439eae8516e DE-627 ger DE-627 rakwb chi TN1-997 Tao ZHOU verfasserin aut Classification of coal gangue pile vegetation based on UAV remote sensing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 0.8780, which was 9.74% and 0.1265 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 0.0845 and 0.0541, 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. uav remote sensing coal gangue pile vegetation classification color space conversion texture filtering multi-feature priority selection Mining engineering. Metallurgy Zhenqi HU verfasserin aut Mengying RUAN verfasserin aut Shuguang LIU verfasserin aut Yuhang ZHANG verfasserin aut In Meitan kexue jishu Editorial Department of Coal Science and Technology, 2022 51(2023), 5, Seite 245-259 (DE-627)588190470 (DE-600)2469839-8 02532336 nnns volume:51 year:2023 number:5 pages:245-259 https://doi.org/10.13199/j.cnki.cst.2021-0899 kostenfrei https://doaj.org/article/e05c2f8fd8544a8e922e2439eae8516e kostenfrei http://www.mtkxjs.com.cn/article/doi/10.13199/j.cnki.cst.2021-0899 kostenfrei https://doaj.org/toc/0253-2336 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_2055 AR 51 2023 5 245-259 |
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10.13199/j.cnki.cst.2021-0899 doi (DE-627)DOAJ100976298 (DE-599)DOAJe05c2f8fd8544a8e922e2439eae8516e DE-627 ger DE-627 rakwb chi TN1-997 Tao ZHOU verfasserin aut Classification of coal gangue pile vegetation based on UAV remote sensing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 0.8780, which was 9.74% and 0.1265 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 0.0845 and 0.0541, 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. uav remote sensing coal gangue pile vegetation classification color space conversion texture filtering multi-feature priority selection Mining engineering. Metallurgy Zhenqi HU verfasserin aut Mengying RUAN verfasserin aut Shuguang LIU verfasserin aut Yuhang ZHANG verfasserin aut In Meitan kexue jishu Editorial Department of Coal Science and Technology, 2022 51(2023), 5, Seite 245-259 (DE-627)588190470 (DE-600)2469839-8 02532336 nnns volume:51 year:2023 number:5 pages:245-259 https://doi.org/10.13199/j.cnki.cst.2021-0899 kostenfrei https://doaj.org/article/e05c2f8fd8544a8e922e2439eae8516e kostenfrei http://www.mtkxjs.com.cn/article/doi/10.13199/j.cnki.cst.2021-0899 kostenfrei https://doaj.org/toc/0253-2336 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_2055 AR 51 2023 5 245-259 |
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10.13199/j.cnki.cst.2021-0899 doi (DE-627)DOAJ100976298 (DE-599)DOAJe05c2f8fd8544a8e922e2439eae8516e DE-627 ger DE-627 rakwb chi TN1-997 Tao ZHOU verfasserin aut Classification of coal gangue pile vegetation based on UAV remote sensing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 0.8780, which was 9.74% and 0.1265 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 0.0845 and 0.0541, 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. uav remote sensing coal gangue pile vegetation classification color space conversion texture filtering multi-feature priority selection Mining engineering. Metallurgy Zhenqi HU verfasserin aut Mengying RUAN verfasserin aut Shuguang LIU verfasserin aut Yuhang ZHANG verfasserin aut In Meitan kexue jishu Editorial Department of Coal Science and Technology, 2022 51(2023), 5, Seite 245-259 (DE-627)588190470 (DE-600)2469839-8 02532336 nnns volume:51 year:2023 number:5 pages:245-259 https://doi.org/10.13199/j.cnki.cst.2021-0899 kostenfrei https://doaj.org/article/e05c2f8fd8544a8e922e2439eae8516e kostenfrei http://www.mtkxjs.com.cn/article/doi/10.13199/j.cnki.cst.2021-0899 kostenfrei https://doaj.org/toc/0253-2336 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_2055 AR 51 2023 5 245-259 |
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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 0.8780, which was 9.74% and 0.1265 higher than that of RGB image of the same period, respectively. And, the accuracy of MLC and NNC classification methods was less improved. 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Classification of coal gangue pile vegetation based on UAV remote sensing |
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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 0.8780, which was 9.74% and 0.1265 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 0.0845 and 0.0541, 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. |
| abstractGer |
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 0.8780, which was 9.74% and 0.1265 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 0.0845 and 0.0541, 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. |
| abstract_unstemmed |
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 0.8780, which was 9.74% and 0.1265 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 0.0845 and 0.0541, 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. |
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