Landslide Identification Method Based on the FKGRNet Model for Remote Sensing Images
Currently, researchers commonly use convolutional neural network (CNN) models for landslide remote sensing image recognition. However, with the increase in landslide monitoring data, the available multimodal landslide data contain rich feature information, and existing landslide recognition models h...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/13/3407 |
| _version_ | 1827734581741092864 |
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| author | Bing Xu Chunju Zhang Wencong Liu Jianwei Huang Yujiao Su Yucheng Yang Weijie Jiang Wenhao Sun |
| author_facet | Bing Xu Chunju Zhang Wencong Liu Jianwei Huang Yujiao Su Yucheng Yang Weijie Jiang Wenhao Sun |
| author_sort | Bing Xu |
| collection | DOAJ |
| description | Currently, researchers commonly use convolutional neural network (CNN) models for landslide remote sensing image recognition. However, with the increase in landslide monitoring data, the available multimodal landslide data contain rich feature information, and existing landslide recognition models have difficulty utilizing such data. A knowledge graph is a linguistic network knowledge base capable of storing and describing various entities and their relationships. A landslide knowledge graph is used to manage multimodal landslide data, and by integrating this graph into a landslide image recognition model, the given multimodal landslide data can be fully utilized for landslide identification. In this paper, we combine knowledge and models, introduce the use of landslide knowledge graphs in landslide identification, and propose a landslide identification method for remote sensing images that fuses knowledge graphs and ResNet (FKGRNet). We take the Loess Plateau of China as the study area and test the effect of the fusion model by comparing the baseline model, the fusion model and other deep learning models. The experimental results show that, first, with ResNet34 as the baseline model, the FKGRNet model achieves 95.08% accuracy in landslide recognition, which is better than that of the baseline model and other deep learning models. Second, the FKGRNet model with different network depths has better landslide recognition accuracy than its corresponding baseline model. Third, the FKGRNet model based on feature splicing outperforms the fused feature classifier in terms of both accuracy and F1-score on the landslide recognition task. Therefore, the FKGRNet model can make fuller use of landslide knowledge to accurately recognize landslides in remote sensing images. |
| first_indexed | 2024-03-11T01:30:37Z |
| format | Article |
| id | doaj.art-b1a6a2dbd28a4be987f26133bc09e56f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T01:30:37Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b1a6a2dbd28a4be987f26133bc09e56f2023-11-18T17:25:50ZengMDPI AGRemote Sensing2072-42922023-07-011513340710.3390/rs15133407Landslide Identification Method Based on the FKGRNet Model for Remote Sensing ImagesBing Xu0Chunju Zhang1Wencong Liu2Jianwei Huang3Yujiao Su4Yucheng Yang5Weijie Jiang6Wenhao Sun7College of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaCollege of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaCollege of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaCollege of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Landscape and Ecological Engineering, Hebei University of Engineering, Handan 056038, ChinaCollege of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaCollege of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaCollege of Civil Engineering, Hefei University of Technology, Hefei 230009, ChinaCurrently, researchers commonly use convolutional neural network (CNN) models for landslide remote sensing image recognition. However, with the increase in landslide monitoring data, the available multimodal landslide data contain rich feature information, and existing landslide recognition models have difficulty utilizing such data. A knowledge graph is a linguistic network knowledge base capable of storing and describing various entities and their relationships. A landslide knowledge graph is used to manage multimodal landslide data, and by integrating this graph into a landslide image recognition model, the given multimodal landslide data can be fully utilized for landslide identification. In this paper, we combine knowledge and models, introduce the use of landslide knowledge graphs in landslide identification, and propose a landslide identification method for remote sensing images that fuses knowledge graphs and ResNet (FKGRNet). We take the Loess Plateau of China as the study area and test the effect of the fusion model by comparing the baseline model, the fusion model and other deep learning models. The experimental results show that, first, with ResNet34 as the baseline model, the FKGRNet model achieves 95.08% accuracy in landslide recognition, which is better than that of the baseline model and other deep learning models. Second, the FKGRNet model with different network depths has better landslide recognition accuracy than its corresponding baseline model. Third, the FKGRNet model based on feature splicing outperforms the fused feature classifier in terms of both accuracy and F1-score on the landslide recognition task. Therefore, the FKGRNet model can make fuller use of landslide knowledge to accurately recognize landslides in remote sensing images.https://www.mdpi.com/2072-4292/15/13/3407knowledge graphlandslide identificationdeep learning modelsfusion modelsremote sensing images |
| spellingShingle | Bing Xu Chunju Zhang Wencong Liu Jianwei Huang Yujiao Su Yucheng Yang Weijie Jiang Wenhao Sun Landslide Identification Method Based on the FKGRNet Model for Remote Sensing Images Remote Sensing knowledge graph landslide identification deep learning models fusion models remote sensing images |
| title | Landslide Identification Method Based on the FKGRNet Model for Remote Sensing Images |
| title_full | Landslide Identification Method Based on the FKGRNet Model for Remote Sensing Images |
| title_fullStr | Landslide Identification Method Based on the FKGRNet Model for Remote Sensing Images |
| title_full_unstemmed | Landslide Identification Method Based on the FKGRNet Model for Remote Sensing Images |
| title_short | Landslide Identification Method Based on the FKGRNet Model for Remote Sensing Images |
| title_sort | landslide identification method based on the fkgrnet model for remote sensing images |
| topic | knowledge graph landslide identification deep learning models fusion models remote sensing images |
| url | https://www.mdpi.com/2072-4292/15/13/3407 |
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