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Articles

A landslide extraction method of channel attention mechanism U-Net network based on Sentinel-2A remote sensing images

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Pages 552-577 | Received 07 Nov 2022, Accepted 01 Feb 2023, Published online: 01 Mar 2023

References

  • Abdollahi, Abolfazl, and Biswajeet Pradhan. 2021. “Integrating Semantic Edges and Segmentation Information for Building Extraction from Aerial Images Using UNet.” Machine Learning with Applications 6: 100194. doi:10.1016/j.mlwa.2021.100194.
  • Blaschke, T., B. Feizizadeh, and D. Hölbling. 2014. “Object-Based Image Analysis and Digital Terrain Analysis for Locating Landslides in the Urmia Lake Basin, Iran.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (12): 4806–4817. doi:10.1109/JSTARS.2014.2350036.
  • Bragagnolo, L., L. R. Rezende, R. V. da Silva, and J. M. V. Grzybowski. 2021. “Convolutional Neural Networks Applied to Semantic Segmentation of Landslide Scars.” CATENA 201: 105189. doi:10.1016/j.catena.2021.105189.
  • Chang, Hui, Rao Zhiqiang, Yulin Zhao, and Yichen Li. 2021. “Research on Tunnel Crack Segmentation Algorithm Based on Improved U-Net Network.” Computer Engineering and Applications Journal 57 (22): 215–222. doi:10.3778/j.issn.1002-8331.2007-0223.
  • Chen, Tao, John C. Trinder, and Ruiqing Niu. 2017. “Object-Oriented Landslide Mapping Using ZY-3 Satellite Imagery, Random Forest and Mathematical Morphology, for the Three-Gorges Reservoir, China.” Remote Sensing 9 (4): 333. doi:10.3390/rs9040333
  • Chen, Ximing, Xin Yao, Zhenkai Zhou, Yang Liu, Chuangchuang Yao, and Kaiyu Ren. 2022. “DRs-UNet: A Deep Semantic Segmentation Network for the Recognition of Active Landslides from InSAR Imagery in the Three Rivers Region of the Qinghai–Tibet Plateau.” Remote Sensing 14 (8): 1848. doi:10.3390/rs14081848
  • Cheng, Libo, Jia Li, Ping Duan, and Mingguo Wang. 2021. “A Small Attentional YOLO Model for Landslide Detection from Satellite Remote Sensing Images.” Landslides 18 (8): 2751–2765. doi:10.1007/s10346-021-01694-6.
  • Dong, Zhangyu, Sen An, Jin Zhang, Jinqiu Yu, Jinhui Li, and Daoli Xu. 2022. “L-Unet: A Landslide Extraction Model Using Multi-Scale Feature Fusion and Attention Mechanism.” Remote Sensing 14 (11): 2552. doi:10.3390/rs14112552
  • Gao, Jinfeng, Yu Chen, Yongming Wei, and Jiannan Li. 2021. “Detection of Specific Building in Remote Sensing Images Using a Novel YOLO-S-CIOU Model. Case: Gas Station Identification.” Sensors 21 (4): 1375. doi:10.3390/s21041375
  • Ghorbanzadeh, Omid, Thomas Blaschke, Khalil Gholamnia, Sansar Raj Meena, Dirk Tiede, and Jagannath Aryal. 2019. “Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection.” Remote Sensing 11 (2): 196. doi:10.3390/rs11020196
  • Ghorbanzadeh, Omid, Khalil Gholamnia, and Pedram Ghamisi. 2022. “The Application of ResU-net and OBIA for Landslide Detection from Multi-Temporal Sentinel-2 Images.” Big Earth Data, 1–26. doi:10.1080/20964471.2022.2031544.
  • Goel, A., C. Tung, Y. H. Lu, and G. K. Thiruvathukal. 2020. “A Survey of Methods for Low-Power Deep Learning and Computer Vision.” Paper presented at the 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), June 2–16.
  • Guo, Meng-Hao, Tian-Xing Xu, Jiang-Jiang Liu, Zheng-Ning Liu, Peng-Tao Jiang, Tai-Jiang Mu, Song-Hai Zhang, Ralph R. Martin, Ming-Ming Cheng, and Shi-Min Hu. 2022. “Attention Mechanisms in Computer Vision: A Survey.” Computational Visual Media 8 (3): 331–368. doi:10.1007/s41095-022-0271-y.
  • Guzzetti, Fausto, Alessandro Cesare Mondini, Mauro Cardinali, Federica Fiorucci, Michele Santangelo, and Kang-Tsung Chang. 2012. “Landslide Inventory Maps: New Tools for an Old Problem.” Earth-Science Reviews 112 (1): 42–66. doi:10.1016/j.earscirev.2012.02.001.
  • He, Y., S. Yao, W. Yang, H. Yan, L. Zhang, Z. Wen, Y. Zhang, and T. Liu. 2021. “An Extraction Method for Glacial Lakes Based on Landsat-8 Imagery Using an Improved U-Net Network.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 6544–6558. doi:10.1109/JSTARS.2021.3085397.
  • Hu, Jie, Li Shen, and Gang Sun. 2018. “Squeeze-and-Excitation Networks.” Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Keyport, Ren N., Thomas Oommen, Tapas R. Martha, K. S. Sajinkumar, and John S. Gierke. 2018. “A Comparative Analysis of Pixel- and Object-Based Detection of Landslides from Very High-Resolution Images.” International Journal of Applied Earth Observation and Geoinformation 64: 1–11. doi:10.1016/j.jag.2017.08.015.
  • Kingma, Diederik P, and Jimmy Ba. 2014. “Adam: A Method for Stochastic Optimization.” arXiv preprint arXiv:1412.6980.
  • Lei, T., Y. Zhang, Z. Lv, S. Li, S. Liu, and A. K. Nandi. 2019. “Landslide Inventory Mapping from Bitemporal Images Using Deep Convolutional Neural Networks.” IEEE Geoscience and Remote Sensing Letters 16 (6): 982–986. doi:10.1109/LGRS.2018.2889307.
  • Li, Huajin, Yusen He, Qiang Xu, Jiahao Deng, Weile Li, and Yong Wei. 2022. “Detection and Segmentation of Loess Landslides via Satellite Images: A Two-Phase Framework.” Landslides 19 (3): 673–686. doi:10.1007/s10346-021-01789-0.
  • Li, Zhongbin, Wenzhong Shi, Ping Lu, Lin Yan, Qunming Wang, and Zelang Miao. 2016. “Landslide Mapping from Aerial Photographs Using Change Detection-Based Markov Random Field.” Remote Sensing of Environment 187: 76–90. doi:10.1016/j.rse.2016.10.008.
  • Li, Zhongbin, Wenzhong Shi, Soe W. Myint, Ping Lu, and Qunming Wang. 2016. “Semi-automated Landslide Inventory Mapping from Bitemporal Aerial Photographs Using Change Detection and Level set Method.” Remote Sensing of Environment 175: 215–230. doi:10.1016/j.rse.2016.01.003.
  • Liu, Zhenqing, Yiwen Cao, Yize Wang, and Wei Wang. 2019. “Computer Vision-Based Concrete Crack Detection Using U-net Fully Convolutional Networks.” Automation in Construction 104: 129–139. doi:10.1016/j.autcon.2019.04.005.
  • Liu, Peng, Yongming Wei, Qinjun Wang, Yu Chen, and Jingjing Xie. 2020. “Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model.” Remote Sensing 12 (5): 894. doi:10.3390/rs12050894.
  • Liu, Ying, and Linzhi Wu. 2016. “Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning.” Procedia Computer Science 91: 566–575. doi:10.1016/j.procs.2016.07.144.
  • Long, Jonathan, Evan Shelhamer, and Trevor Darrell. 2015. “Fully Convolutional Networks for Semantic Segmentation.” Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Martha, Tapas R., Norman Kerle, Cees J. van Westen, Victor Jetten, and K. Vinod Kumar. 2012. “Object-oriented Analysis of Multi-Temporal Panchromatic Images for Creation of Historical Landslide Inventories.” ISPRS Journal of Photogrammetry and Remote Sensing 67: 105–119. doi:10.1016/j.isprsjprs.2011.11.004.
  • Mei, H., and J. X. Zhang. 2010. “Types and Distribution of Geological Hazards in Lanzhou City.” South-to-North Water Transfers and Water Science & Technology 8 (4): 53–57. doi:10.3724/SP.J.1201.2010.04053.
  • Mohan, Amrita, Amit Kumar Singh, Basant Kumar, and Ramji Dwivedi. 2021. “Review on Remote Sensing Methods for Landslide Detection Using Machine and Deep Learning.” Transactions on Emerging Telecommunications Technologies 32 (7): e3998. doi:10.1002/ett.3998.
  • Niu, Zhaoyang, Guoqiang Zhong, and Hui Yu. 2021. “Adaptive Cooperative Dynamic Surface Control of Non-Strict Feedback Multi-Agent Systems with Input Dead-Zones and Actuator Failures.” Neurocomputing 442: 48–63. doi:10.1016/j.neucom.2021.02.039.
  • Oktay, Ozan, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y Hammerla, and Bernhard Kainz. 2018. “Attention U-Net: Learning Where to Look for the Pancreas.” arXiv preprint arXiv:1804.03999.
  • Pang, Yanwei, Yazhao Li, Jianbing Shen, and Ling Shao. 2019. “Towards Bridging Semantic Gap to Improve Semantic Segmentation.” Paper presented at the Proceedings of the IEEE/CVF International Conference on Computer Vision.
  • Prakash, Nikhil, Andrea Manconi, and Simon Loew. 2020. “Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models.” Remote Sensing 12 (3): 346. doi:10.3390/rs12030346
  • Qi, Wenwen, Mengfei Wei, Wentao Yang, Chong Xu, and Chao Ma. 2020. “Automatic Mapping of Landslides by the ResU-Net.” Remote Sensing 12 (15): 2487. doi:10.3390/rs12152487
  • Quader, Niamul, Md Mafijul Islam Bhuiyan, Juwei Lu, Peng Dai, and Wei Li. 2020. “Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural Networks.” Paper presented at the Computer Vision – ECCV 2020, Cham, 2020//.
  • Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” Paper presented at the Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Cham, 2015//.
  • Sarkar, Tanmoy, and Mukunda Mishra. 2018. “Soil Erosion Susceptibility Mapping with the Application of Logistic Regression and Artificial Neural Network.” Journal of Geovisualization and Spatial Analysis 2 (1): 8. doi:10.1007/s41651-018-0015-9.
  • Shamsolmoali, P., M. Zareapoor, R. Wang, H. Zhou, and J. Yang. 2019. “A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (9): 3219–3232. doi:10.1109/JSTARS.2019.2925841.
  • Soares, Lucas P, Helen C. Dias, and Carlos H Grohmann. 2020. “Landslide Segmentation with U-Net: Evaluating Different Sampling Methods and Patch Sizes.” arXiv preprint arXiv:2007.06672.
  • Suzuki, Y., and S. Yamane. 2020. “Transfer Learning Model for Image Segmentation by Integrating U-Net ++ and SE Block.” Paper presented at the 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), October 13–16.
  • Thomas, Anjana V., Sunil Saha, Jean Homian Danumah, S. Raveendran, Megha K. Prasad, R. S. Ajin, and Sekhar L. Kuriakose. 2021. “Landslide Susceptibility Zonation of Idukki District Using GIS in the Aftermath of 2018 Kerala Floods and Landslides: A Comparison of AHP and Frequency Ratio Methods.” Journal of Geovisualization and Spatial Analysis 5 (2): 21. doi:10.1007/s41651-021-00090-x.
  • Tien Bui, Dieu, Nhat-Duc Hoang, Francisco Martínez-Álvarez, Phuong-Thao Thi Ngo, Pham Viet Hoa, Tien Dat Pham, Pijush Samui, and Romulus Costache. 2020. “A Novel Deep Learning Neural Network Approach for Predicting Flash Flood Susceptibility: A Case Study at a High Frequency Tropical Storm Area.” Science of The Total Environment 701: 134413. doi:10.1016/j.scitotenv.2019.134413.
  • Trinh, Thanh, Binh Thanh Luu, Trang Ha Thi Le, Duong Huy Nguyen, Trong Van Tran, Thi Hai Van Nguyen, Khanh Quoc Nguyen, and Lien Thi Nguyen. 2022. “A Comparative Analysis of Weight-Based Machine Learning Methods for Landslide Susceptibility Mapping in Ha Giang Area.” Big Earth Data, 1–30. doi:10.1080/20964471.2022.2043520
  • Wandong, Jiang, Xi Jiangbo, Li Zhenhong, Ding Mingtao, Yang Ligong, and Xie Dashuai. 2021. “Landslide Detection and Segmentation Using Mask R-CNN with Simulated Hard Samples.” Geomatics and Information Science of Wuhan University, 1–18. doi:10.13203/j.whugis20200692.
  • Wang, Haonan, Peng Cao, Jiaqi Wang, and Osmar R. Zaiane. 2022. “UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer.” Proceedings of the AAAI Conference on Artificial Intelligence 36 (3): 2441–2449. doi:10.1609/aaai.v36i3.20144.
  • Wang, Haojie, Limin Zhang, Kesheng Yin, Hongyu Luo, and Jinhui Li. 2021. “Landslide Identification Using Machine Learning.” Geoscience Frontiers 12 (1): 351–364. doi:10.1016/j.gsf.2020.02.012.
  • Xiao, Xiao, Fan Yang, and Amir Sadovnik. 2021. “MSDU-Net: A Multi-Scale Dilated U-Net for Blur Detection.” Sensors 21 (5): 1873. doi:10.3390/s21051873
  • Xu, Chong, F. C. Dai, Jian Chen, X. B. Tu, Ling Xu, Wei Chao Li, W. Tian, Y. B. Cao, and X. Yao. 2009. “Identification and Analysis of Secondary Geological Hazards Triggered by a Magnitude 8.0 Wenchuan Earthquake.” Journal of Remote Sensing 13 (04): 754–762. doi:10.11834/jrs.20090416.
  • Yang, Xiao. 2020. “An Overview of the Attention Mechanisms in Computer Vision.” Journal of Physics: Conference Series 1693 (1): 012173. doi:10.1088/1742-6596/1693/1/012173.
  • Yang, W., M. Wang, and P. Shi. 2013. “Using MODIS NDVI Time Series to Identify Geographic Patterns of Landslides in Vegetated Regions.” IEEE Geoscience and Remote Sensing Letters 10 (4): 707–710. doi:10.1109/LGRS.2012.2219576.
  • Yi, Y., and W. Zhang. 2020. “A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection from Single-Temporal RapidEye Satellite Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 6166–6176. doi:10.1109/JSTARS.2020.3028855.
  • Yi, Yaning, Zhijie Zhang, Wanchang Zhang, Chuanrong Zhang, Weidong Li, and Tian Zhao. 2019. “Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network.” Remote Sensing 11 (15): 1774. doi:10.3390/rs11151774
  • Zhang, Pengfei, Chong Xu, Siyuan Ma, Xiaoyi Shao, Yingying Tian, and Boyu Wen. 2020. “Automatic Extraction of Seismic Landslides in Large Areas with Complex Environments Based on Deep Learning: An Example of the 2018 Iburi Earthquake, Japan.” Remote Sensing 12 (23): 3992. doi:10.3390/rs12233992
  • Zhang, L., L. Zhang, and B. Du. 2016. “Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art.” IEEE Geoscience and Remote Sensing Magazine 4 (2): 22–40. doi:10.1109/MGRS.2016.2540798.
  • Zhao, W., A. Li, X. Nan, Z. Zhang, and G. Lei. 2017. “Postearthquake Landslides Mapping from Landsat-8 Data for the 2015 Nepal Earthquake Using a Pixel-Based Change Detection Method.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (5): 1758–1768. doi:10.1109/JSTARS.2017.2661802.
  • Zhu, Li, Lianghao Huang, Linyu Fan, Jinsong Huang, Faming Huang, Jiawu Chen, Zihe Zhang, and Yuhao Wang. 2020. “Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network.” Sensors 20 (6): 1576. doi:10.3390/s20061576