Deep Learning Method of Landslide Inventory Map with Imbalanced Samples in Optical Remote Sensing
Landslide inventory mapping (LIM) is a key prerequisite for landslide susceptibility evaluation and disaster mitigation. It aims to record the location, size, and extent of landslides in each map scale. Machine learning algorithms, such as support vector machine (SVM) and random forest (RF), have be...
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| Format: | Article |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/21/5517 |
| _version_ | 1797466625768685568 |
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| author | Xuerong Chen Chaoying Zhao Jiangbo Xi Zhong Lu Shunping Ji Liquan Chen |
| author_facet | Xuerong Chen Chaoying Zhao Jiangbo Xi Zhong Lu Shunping Ji Liquan Chen |
| author_sort | Xuerong Chen |
| collection | DOAJ |
| description | Landslide inventory mapping (LIM) is a key prerequisite for landslide susceptibility evaluation and disaster mitigation. It aims to record the location, size, and extent of landslides in each map scale. Machine learning algorithms, such as support vector machine (SVM) and random forest (RF), have been increasingly applied to landslide detection using remote sensing images in recent decades. However, their limitations have impeded their wide application. Furthermore, despite the widespread use of deep learning algorithms in remote sensing, for LIM, deep learning algorithms are limited to less unbalanced landslide samples. To this end, in this study, full convolution networks with focus loss (FCN-FL) were adopted to map historical landslides in regions with imbalanced samples using an improved symmetrically connected full convolution network and focus loss function to increase the feature level and reduce the contribution of the background loss value. In addition, K-fold cross-validation training models (FCN-FLK) were used to improve data utilization and model robustness. Results showed that the recall rate, F1-score, and mIoU of the model were improved by 0.08, 0.09, and 0.15, respectively, compared to FCN. It also demonstrated advantages over U-Net and SegNet. The results prove that the method proposed in this study can solve the problem of imbalanced sample in landslide inventory mapping. This research provides a reference for addressing imbalanced samples in the deep learning of LIM. |
| first_indexed | 2024-03-09T18:41:20Z |
| format | Article |
| id | doaj.art-b5bedea148f04029af8d002e35c04fb4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:41:20Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b5bedea148f04029af8d002e35c04fb42023-11-24T06:40:35ZengMDPI AGRemote Sensing2072-42922022-11-011421551710.3390/rs14215517Deep Learning Method of Landslide Inventory Map with Imbalanced Samples in Optical Remote SensingXuerong Chen0Chaoying Zhao1Jiangbo Xi2Zhong Lu3Shunping Ji4Liquan Chen5School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaSchool of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaSchool of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaRoy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75275, USASchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaLandslide inventory mapping (LIM) is a key prerequisite for landslide susceptibility evaluation and disaster mitigation. It aims to record the location, size, and extent of landslides in each map scale. Machine learning algorithms, such as support vector machine (SVM) and random forest (RF), have been increasingly applied to landslide detection using remote sensing images in recent decades. However, their limitations have impeded their wide application. Furthermore, despite the widespread use of deep learning algorithms in remote sensing, for LIM, deep learning algorithms are limited to less unbalanced landslide samples. To this end, in this study, full convolution networks with focus loss (FCN-FL) were adopted to map historical landslides in regions with imbalanced samples using an improved symmetrically connected full convolution network and focus loss function to increase the feature level and reduce the contribution of the background loss value. In addition, K-fold cross-validation training models (FCN-FLK) were used to improve data utilization and model robustness. Results showed that the recall rate, F1-score, and mIoU of the model were improved by 0.08, 0.09, and 0.15, respectively, compared to FCN. It also demonstrated advantages over U-Net and SegNet. The results prove that the method proposed in this study can solve the problem of imbalanced sample in landslide inventory mapping. This research provides a reference for addressing imbalanced samples in the deep learning of LIM.https://www.mdpi.com/2072-4292/14/21/5517landslide inventory mappingfully convolutional networksfocal lossK-fold cross-validation |
| spellingShingle | Xuerong Chen Chaoying Zhao Jiangbo Xi Zhong Lu Shunping Ji Liquan Chen Deep Learning Method of Landslide Inventory Map with Imbalanced Samples in Optical Remote Sensing Remote Sensing landslide inventory mapping fully convolutional networks focal loss K-fold cross-validation |
| title | Deep Learning Method of Landslide Inventory Map with Imbalanced Samples in Optical Remote Sensing |
| title_full | Deep Learning Method of Landslide Inventory Map with Imbalanced Samples in Optical Remote Sensing |
| title_fullStr | Deep Learning Method of Landslide Inventory Map with Imbalanced Samples in Optical Remote Sensing |
| title_full_unstemmed | Deep Learning Method of Landslide Inventory Map with Imbalanced Samples in Optical Remote Sensing |
| title_short | Deep Learning Method of Landslide Inventory Map with Imbalanced Samples in Optical Remote Sensing |
| title_sort | deep learning method of landslide inventory map with imbalanced samples in optical remote sensing |
| topic | landslide inventory mapping fully convolutional networks focal loss K-fold cross-validation |
| url | https://www.mdpi.com/2072-4292/14/21/5517 |
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