Citation: | Wu Qi, Zhou Chuangbing, Huang Faming, Yao Chi. Optimization of the landslide identification method based on a dual attention mechanism[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 246-253. doi: 10.19509/j.cnki.dzkq.2022.0053 |
[1] |
黄润秋. 20世纪以来中国的大型滑坡及其发生机制[J]. 岩石力学与工程学报, 2007, 26(3): 433-454. doi: 10.3321/j.issn:1000-6915.2007.03.001
Huang R C. Large scale landslides and their occurrence mechanism in China since the 20th century[J]. Chinese Journal of Rock Mechanics and Engineering, 2007, 26(3): 433-454(in Chinese with English abstract). doi: 10.3321/j.issn:1000-6915.2007.03.001
|
[2] |
陈红旗. 贵州纳雍张家湾普洒村山体崩塌[J]. 中国地质灾害与防治学报, 2018, 29(1): 22. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH201801025.htm
Cheng H Q. Mountain collapse in Pusa Village, Zhangjiawan, Nayong, Guizhou[J]. The Chinese Journal of Geological Hazard and Control, 2018, 29(1): 22(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH201801025.htm
|
[3] |
陈红旗. 四川茂县"6·24"特大山体滑坡灾害[J]. 中国地质灾害与防治学报, 2017, 28(3): 51. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH201703026.htm
Cheng H Q. "6.24" landslide disaster in Maoxian County, Sichuan Province[J]. The Chinese Journal of Geological Hazard and Control, 2017, 28(3): 51(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH201703026.htm
|
[4] |
张卫雄, 翟向华, 丁保艳, 等. 甘肃舟曲江顶崖滑坡成因分析与综合治理措施[J]. 中国地质灾害与防治学报, 2020, 31(5): 7-14. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202005002.htm
Zhang W X, Zhai X H, Ding B Y, et al. Causative analysis and comprehensive treatment of the Jiangdingya landslide in Zhouqu County of Gansu Province[J]. The Chinese Journal of Geological Hazard and Control, 2020, 31(5): 7-14(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202005002.htm
|
[5] |
Bellotti F, Bianchi M, Colombo D, et al. Advanced InSAR techniques to support landslide monitoring[C]//Anon. 15th Annual Conference of the International Association of Mathematical Geosciences. [S.l.]: [s.n.], 2013: 287-290.
|
[6] |
Escanoglu M, Gokceoglu C. Assessment of landslide susceptibility for a landslide-prone area(north of Yenice, NW Turkey)by fuzzy approach[J]. Environmental Geology, 2002, 41(6): 720-730. doi: 10.1007/s00254-001-0454-2
|
[7] |
胡涛, 樊鑫, 王硕, 等. 基于逻辑回归模型和3S技术的思南县滑坡易发性评价[J]. 地质科技通报, 2020, 39(2): 113-121. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202002013.htm
Hu T, Fan X, Wang S, et al. Landslide susceptibility evaluation of Sinan County using logistics regression model and 3S technology[J]. Bulletin of Geological Science and Technology, 2020, 39(2): 113-121(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202002013.htm
|
[8] |
Kayastha P, Dhital M R, Smedt F D. Application of the analytical hierarchy process(AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal[J]. Computers & Geosciences, 2013, 52(3): 398-408.
|
[9] |
Althuwaynee O F, Pradhan B, Lee S. Application of an evidential belief function model in landslide susceptibility mapping[J]. Computers & Geosciences, 2012, 44: 120-135. https://www.sciencedirect.com/science/article/pii/S009830041200091X
|
[10] |
Komac M. A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia-Science Direct[J]. Geomorphology, 2006, 74(1): 17-28. https://www.sciencedirect.com/science/article/pii/S0169555X05002072
|
[11] |
Lee S. Application and verification of fuzzy algebraic operators to landslide susceptibility mapping[J]. Environmental Geology, 2007, 52(4): 615-623. doi: 10.1007/s00254-006-0491-y
|
[12] |
Marjanovi M, Kovaevi M, Bajat B, et al. Landslide susceptibility assessment using SVM machine learning algorithm[J]. Engineering Geology, 2011, 123(3): 225-234. doi: 10.1016/j.enggeo.2011.09.006
|
[13] |
黄发明, 胡松雁, 闫学涯, 等. 基于机器学习的滑坡易发性预测建模及其主控因子识别[J/OL]. 地质科技通报: 1-12[2021-09-30]. https://doi.org/10.19509/j.cnki.dzkq.2021.0087.
Huang F M, Hu S Y, Yan X Y, et al. Landslide susceptibility prediction and its main environmental factors identification based on machine learning models[J/OL]. Bulletin of Geological Science and Technology: 1-12[2021-09-30]. https://doi.org/10.19509/j.cnki.dzkq.2021.0087(in Chinese with English abstract).
|
[14] |
郑迎凯, 陈建国, 王成彬, 等. 确定性系数与随机森林模型在云南芒市滑坡易发性评价中的应用[J]. 地质科技通报, 2020, 39(6): 131-144. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202006015.htm
Zheng Y K, Chen J G, Wang C B, et al. Application of certainty factor and random forest model in landslide susceptibility evaluation in Mangshi City, Yunnan Province[J]. Bulletin of Geological Science and Technology, 2020, 39(6): 131-144(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ202006015.htm
|
[15] |
Hong Y, Yi M, Wang L, et al. A landslide intelligent detection method based on CNN and RSG_R[C]//Anon. IEEE International Conference on Mechatronics & Automation. [S.l.]: IEEE, 2017: 40-44.
|
[16] |
Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521: 436-444. https://www.nature.com/articles/nature14539
|
[17] |
He K, Zhang X, Ren S, et al. Deep Residual learning for image recognition[C]//Anon. 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). [S.l.]: IEEE, 2016: 770-778.
|
[18] |
Ren S Q, He K M, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. doi: 10.1109/TPAMI.2016.2577031
|
[19] |
Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848. https://arxiv.org/abs/1606.00915
|
[20] |
Ding A, Zhang Q, Zhou X, et al. Automatic recognition of landslide based on CNN and texture change detection[C]//Anon. 201631st Youth Academic Annual Conference of Chinese Association of Automation(YAC). [S.l.]: IEEE, 2016: 444-448.
|
[21] |
Ghorbanzadeh O, Blaschke T, Gholamnia K, et al. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection[J]. Remote Sensing, 2019, 11(2): 196-216. https://www.researchgate.net/publication/330514716_Evaluation_of_Different_Machine_Learning_Methods_and_Deep-Learning_Convolutional_Neural_Networks_for_Landslide_Detection
|
[22] |
Jie H, Li S, Gang S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 42(8): 2011-2023. https://ieeexplore.ieee.org/document/8701503/
|
[23] |
Park J, Woo S, Lee J Y, et al. BAM: Bottleneck attention module[C]//Anon. British machine vison conference. Berlin: BMWC, 2018: 147-160.
|
[24] |
Woo S, Park J, Lee J Y, et al. Cbam: Convolutional block attention module[C]//Anon. Proceedings of the European Conference on Computer Vision(ECCV). [S.l.]: [s.n.], 2018: 3-19.
|
[25] |
Fu J, Liu J, Tian H, et al. Dual attention network for scene segmentation[C]//Anon. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). [S.l.]: IEEE, 2020: 3141-3149.
|
[26] |
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv: 1409.1556, 2014.
|
[27] |
Evan S, Jonathan L, Trevor D. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651. https://pubmed.ncbi.nlm.nih.gov/27244717/
|
[28] |
王赛. 基于多源遥感数据的汶川地震型滑坡信息提取研究[D]. 北京: 中国地质大学(北京), 2015.
Wang S. Research on earthquake-induced landslide extraction from multi-source remote sensing data in the Wenchuan[D]. Beijing: China University of Geosciences(Beijing), 2015(in Chinese with English abstract).
|
[29] |
Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]//Anon. 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). [S.l.]: IEEE, 2016: 2818-2826.
|
[30] |
Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network[C]//Anon. 2017 IEEE Conference on Computer Vision and Pattern Recongnition(CVPR). [S.l.]: IEEE, 2017: 6230-6239.
|
[31] |
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Anon. 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). [S.l.]: IEEE, 2016: 770-778.
|
[32] |
Huang G, Liu Z, Laurens V, et al. Densely connected convolutional networks[C]//Anon. IEEE Conference on Computer Vision and Pattern Recognition(CVPR). [S.l.]: IEEE, 2017: 2261-2269.
|