Automatic Rock Classification Algorithm Based on Ensemble Residual Network and Merged Region Extraction
Lithology identification of rocks is an important part in the field of oil and gas exploration, mineral exploration, and geological analysis. How to accomplish rock classification is a key issue for the further development of the geology industry. The current main method for classifying rock picture...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2022/3982892 |
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| _version_ | 1832551260595683328 |
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| author | Chenlu Guo Zhiyuan Li |
| author_facet | Chenlu Guo Zhiyuan Li |
| author_sort | Chenlu Guo |
| collection | DOAJ |
| description | Lithology identification of rocks is an important part in the field of oil and gas exploration, mineral exploration, and geological analysis. How to accomplish rock classification is a key issue for the further development of the geology industry. The current main method for classifying rock pictures containing background is to select sample points or disregard the disturbance of the background. For more accurate classification, the rock part extraction method for rock images containing boundaries is designed to eliminate the influence of background. First, the rock parts are extracted based on the image gradient information and color information, respectively. Then, the two images are intersected to realize the refinement of pixel-level information to obtain a pure rock image. Ensemble ResNet18 (ERN18) is designed as an image classification model. It contains basic blocks to reduce the loss of features during the training process. The method breaks the neglect of most previous studies on background interference. The effect of misclassification in certain regions on the results is eliminated by ensemble learning based on the voting method. The classification results are further improved. Compared with the effects of LeNet, AlexNet, and ResNet, ERN18 has achieved significant results. |
| format | Article |
| id | doaj-art-8021b3f8468540c7a51553f3201b5690 |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-8021b3f8468540c7a51553f3201b56902025-02-03T06:01:52ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/3982892Automatic Rock Classification Algorithm Based on Ensemble Residual Network and Merged Region ExtractionChenlu Guo0Zhiyuan Li1College of Computer Science and EngineeringCollege of Computer Science and EngineeringLithology identification of rocks is an important part in the field of oil and gas exploration, mineral exploration, and geological analysis. How to accomplish rock classification is a key issue for the further development of the geology industry. The current main method for classifying rock pictures containing background is to select sample points or disregard the disturbance of the background. For more accurate classification, the rock part extraction method for rock images containing boundaries is designed to eliminate the influence of background. First, the rock parts are extracted based on the image gradient information and color information, respectively. Then, the two images are intersected to realize the refinement of pixel-level information to obtain a pure rock image. Ensemble ResNet18 (ERN18) is designed as an image classification model. It contains basic blocks to reduce the loss of features during the training process. The method breaks the neglect of most previous studies on background interference. The effect of misclassification in certain regions on the results is eliminated by ensemble learning based on the voting method. The classification results are further improved. Compared with the effects of LeNet, AlexNet, and ResNet, ERN18 has achieved significant results.http://dx.doi.org/10.1155/2022/3982892 |
| spellingShingle | Chenlu Guo Zhiyuan Li Automatic Rock Classification Algorithm Based on Ensemble Residual Network and Merged Region Extraction Advances in Multimedia |
| title | Automatic Rock Classification Algorithm Based on Ensemble Residual Network and Merged Region Extraction |
| title_full | Automatic Rock Classification Algorithm Based on Ensemble Residual Network and Merged Region Extraction |
| title_fullStr | Automatic Rock Classification Algorithm Based on Ensemble Residual Network and Merged Region Extraction |
| title_full_unstemmed | Automatic Rock Classification Algorithm Based on Ensemble Residual Network and Merged Region Extraction |
| title_short | Automatic Rock Classification Algorithm Based on Ensemble Residual Network and Merged Region Extraction |
| title_sort | automatic rock classification algorithm based on ensemble residual network and merged region extraction |
| url | http://dx.doi.org/10.1155/2022/3982892 |
| work_keys_str_mv | AT chenluguo automaticrockclassificationalgorithmbasedonensembleresidualnetworkandmergedregionextraction AT zhiyuanli automaticrockclassificationalgorithmbasedonensembleresidualnetworkandmergedregionextraction |