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Articles

DSFA: cross-scene domain style and feature adaptation for landslide detection from high spatial resolution images

, ORCID Icon, , , &
Pages 2426-2447 | Received 23 Mar 2023, Accepted 21 Jun 2023, Published online: 30 Jun 2023

References

  • Amatya, Pukar, Dalia Kirschbaum, Thomas Stanley, and Hakan Tanyas. 2021. “Landslide Mapping Using Object-Based Image Analysis and Open Source Tools.” Engineering Geology 282:106000. https://doi.org/10.1016/j.enggeo.2021.106000.
  • Azarafza, Mohammad, Mehdi Azarafza, Haluk Akgün, Peter M. Atkinson, and Reza Derakhshani. 2021. “Deep Learning-Based Landslide Susceptibility Mapping.” Scientific Reports 11 (1): 24112. https://doi.org/10.1038/s41598-021-03585-1.
  • Bruckner, Stefan, and M. Eduard Gröller. 2007. “Style Transfer Functions for Illustrative Volume Rendering.” Paper presented at the Computer Graphics Forum.
  • Chen, Kun Shan, Melba M. Crawford, Paolo Gamba, and James S. Smith. 2007. “Introduction for the Special Issue on Remote Sensing for Major Disaster Prevention, Monitoring, and Assessment.” IEEE Transactions on Geoscience and Remote Sensing 45 (6): 1515–1518. https://doi.org/10.1109/TGRS.2007.899144.
  • Chen, Chi Wen, Yu Shiang Tung, Jun Jih Liou, Hsin Chi Li, Chao Tzuen Cheng, Yung Ming Chen, and Takashi Oguchi. 2019. “Assessing Landslide Characteristics in a Changing Climate in Northern Taiwan.” Catena 175:263–277. https://doi.org/10.1016/j.catena.2018.12.023.
  • Deng, Yingying, Fan Tang, Weiming Dong, Chongyang Ma, Xingjia Pan, Lei Wang, and Changsheng Xu. 2022. “Stytr2: Image Style Transfer with Transformers.” Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Ding, Anzi, Qingyong Zhang, Xinmin Zhou, and Bicheng Dai. 2016. “Automatic Recognition of Landslide Based on CNN and Texture Change Detection.” Paper presented at the Youth Academic Annual Conference of Chinese Association of Automation.
  • Dong, Hao, Simiao Yu, Chao Wu, and Yike Guo. 2017. “Semantic Image Synthesis via Adversarial Learning.” Paper presented at the Proceedings of the IEEE international Conference on Computer Vision.
  • Fang, Zhice, Yi Wang, Ling Peng, and Haoyuan Hong. 2020. “Integration of Convolutional Neural Network and Conventional Machine Learning Classifiers for Landslide Susceptibility Mapping.” Computers & Geosciences 139:104470. https://doi.org/10.1016/j.cageo.2020.104470.
  • Fang, Zhice, Yi Wang, Ling Peng, and Haoyuan Hong. 2021. “A Comparative Study of Heterogeneous Ensemble-Learning Techniques for Landslide Susceptibility Mapping.” International Journal of Geographical Information Science 35 (2): 321–347. https://doi.org/10.1080/13658816.2020.1808897.
  • 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. https://doi.org/10.3390/rs11020196.
  • Ghorbanzadeh, Omid, Alessandro Crivellari, Pedram Ghamisi, Hejar Shahabi, and Thomas Blaschke. 2021. “A Comprehensive Transferability Evaluation of U-Net and ResU-Net for Landslide Detection from Sentinel-2 Data (Case Study Areas from Taiwan, China, and Japan).” Scientific Reports 11 (1): 1–20. https://doi.org/10.1038/s41598-021-94190-9.
  • 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. https://doi.org/10.1080/20964471.2022.2031544.
  • Ghorbanzadeh, Omid, Yonghao Xu, Pedram Ghamis, Michael Kopp, and David Kreil. 2022. “Landslide4sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection.” arXiv preprint arXiv:2206.00515.
  • Hacıefendioğlu, Kemal, Gökhan Demir, and Hasan Basri Başağa. 2021. “Landslide Detection Using Visualization Techniques for Deep Convolutional Neural Network Models.” Natural Hazards 109 (1): 329–350. https://doi.org/10.1007/s11069-021-04838-y.
  • Hölbling, Daniel, Petra Füreder, Francesco Antolini, Francesca Cigna, Nicola Casagli, and Stefan Lang. 2012. “A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories.” Remote Sensing 4 (5): 1310–1336. https://doi.org/10.3390/rs4051310.
  • Hoyer, Lukas, Dengxin Dai, and Luc Van Gool. 2022a. “HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation.” Paper presented at the Proceedings of the European Conference on Computer Vision.
  • Hoyer, Lukas, Dengxin Dai, and Luc Van Gool. 2022b. “Daformer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation.” Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Huang, Huimin, Lanfen Lin, Ruofeng Tong, Hongjie Hu, Qiaowei Zhang, Yutaro Iwamoto, Xianhua Han, Yen Wei Chen, and Jian Wu. 2020. “UNet 3+: A Full-Scale Connected Unet for Medical Image Segmentation.” Paper presented at the IEEE International Conference on Acoustics, Speech and Signal Processing.
  • Li, Zhun, and Yonggang Guo. 2020. “Semantic Segmentation of Landslide Images in Nyingchi Region Based on PSPNet Network.” Paper presented at the International Conference on Information Science and Control Engineering.
  • Li, Penglei, Yi Wang, Guosen Xu, and Lizhe Wang. 2023. “LandslideCL: Towards Robust Landslide Analysis Guided by Contrastive Learning.” Landslides 20 (2): 461–474. https://doi.org/10.1007/s10346-022-01981-w.
  • Li, Chang, Bangjin Yi, Peng Gao, Hui Li, Jixing Sun, Xueye Chen, and Cheng Zhong. 2021. “Valuable Clues for DCNN-Based Landslide Detection from a Comparative Assessment in the Wenchuan Earthquake Area.” Sensors 21 (15): 5191. https://doi.org/10.3390/s21155191.
  • Lian, Qing, Fengmao Lv, Lixin Duan, and Boqing Gong. 2019. “Constructing Self-Motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach.” Paper presented at the Proceedings of the IEEE International Conference on Computer Vision.
  • Lin, S. C., M. C. Ke, and C. M. Lo. 2017. “Evolution of Landslide Hotspots in Taiwan.” Landslides 14 (4): 1491–1501. https://doi.org/10.1007/s10346-017-0816-9.
  • Lissak, Candide, Annett Bartsch, Marcello De Michele, Christopher Gomez, Olivier Maquaire, Daniel Raucoules, and Thomas Roulland. 2020. “Remote Sensing for Assessing Landslides and Associated Hazards.” Surveys in Geophysics 41 (6): 1391–1435. https://doi.org/10.1007/s10712-020-09609-1.
  • 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. https://doi.org/10.3390/rs12050894.
  • Long, Mingsheng, Yue Cao, Jianmin Wang, and Michael Jordan. 2015. “Learning Transferable Features with Deep Adaptation Networks.” Paper presented at the International Conference on Machine Learning.
  • Lu, Ping, Shibiao Bai, Veronica Tofani, and Nicola Casagli. 2019. “Landslides Detection through Optimized Hot Spot Analysis on Persistent Scatterers and Distributed Scatterers.” ISPRS Journal of Photogrammetry and Remote Sensing 156:147–159. https://doi.org/10.1016/j.isprsjprs.2019.08.004.
  • Lu, Ping, Yuanyuan Qin, Zhongbin Li, Alessandro C. Mondini, and Nicola Casagli. 2019. “Landslide Mapping from Multi-Sensor Data through Improved Change Detection-Based Markov Random Field.” Remote Sensing of Environment 231:111235. https://doi.org/10.1016/j.rse.2019.111235.
  • Madadi, Yeganeh, Vahid Seydi, Kamal Nasrollahi, Reshad Hosseini, and Thomas B. Moeslund. 2020. “Deep Visual Unsupervised Domain Adaptation for Classification Tasks: A Survey.” IET Image Processing 14 (14): 3283–3299. https://doi.org/10.1049/iet-ipr.2020.0087.
  • Meena, Sansar Raj, Lucas Pedrosa Soares, Carlos H. Grohmann, Cees Van Westen, Kushanav Bhuyan, Ramesh P. Singh, Mario Floris, and Filippo Catani. 2022. “Landslide Detection in the Himalayas Using Machine Learning Algorithms and U-Net.” Landslides 19 (5): 1209–1229. https://doi.org/10.1007/s10346-022-01861-3.
  • Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. 2016. “V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation.” Paper presented at the International Conference on 3D Vision.
  • Netto, Ana Luiza Coelho, Anderson Mululo Sato, André de Souza Avelar, Lílian Gabriela G. Vianna, Ingrid S. Araújo, David L. C. Ferreira, Pedro H. Lima, Ana Paula A. Silva, and Roberta P. Silva. 2013. “January 2011: The Extreme Landslide Disaster in Brazil.” In Landslide Science and Practice: Volume 6: Risk Assessment, Management and Mitigation, 377–384. https://doi.org/10.1007/978-3-642-31319-6-51.
  • Nikoobakht, Shahrzad, Mohammad Azarafza, Haluk Akgün, and Reza Derakhshani. 2022. “Landslide Susceptibility Assessment by Using Convolutional Neural Network.” Applied Sciences 12 (12): 5992. https://doi.org/10.3390/app12125992.
  • Paul, Sayak, and Pin-Yu Chen. 2022. “Vision Transformers Are Robust Learners.” Paper presented at the Proceedings of the AAAI Conference on Artificial Intelligence.
  • Pinheiro, Pedro O. 2018. “Unsupervised Domain Adaptation with Similarity Learning.” Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • 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. https://doi.org/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. https://doi.org/10.3390/rs12152487.
  • Qin, Shengwu, Xu Guo, Jingbo Sun, Shuangshuang Qiao, Lingshuai Zhang, Jingyu Yao, Qiushi Cheng, and Yanqing Zhang. 2021. “Landslide Detection from Open Satellite Imagery Using Distant Domain Transfer Learning.” Remote Sensing 13 (17): 3383. https://doi.org/10.3390/rs13173383.
  • Scaioni, Marco, Laura Longoni, Valentina Melillo, and Monica Papini. 2014. “Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives.” Remote Sensing 6 (10): 9600–9652. https://doi.org/10.3390/rs6109600.
  • Shahabi, Hejar, and Omid Ghorbanzadeh. 2022. “Model-Centric vs Data-Centric Deep Learning Approaches for Landslide Detection.” 1–6. CDCEO 2022: 2nd Workshop on Complex Data Challenges in Earth Observation, July 25, Vienna, Austria.
  • Shamsolmoali, Pourya, Masoumeh Zareapoor, Huiyu Zhou, Ruili Wang, and Jie Yang. 2021. “Road Segmentation for Remote Sensing Images Using Adversarial Spatial Pyramid Networks.” IEEE Transactions on Geoscience and Remote Sensing 59 (6): 4673–4688. https://doi.org/10.1109/TGRS.2020.3016086.
  • Si, Tongzhen, Fazhi He, Penglei Li, and Xiaoxin Gao. 2023. “Tri-Modality Consistency Optimization with Heterogeneous Augmented Images for Visible-Infrared Person Re-Identification.” Neurocomputing 523:170–181. https://doi.org/10.1016/j.neucom.2022.12.042.
  • Si, Tongzhen, Fazhi He, Haoran Wu, and Yansong Duan. 2022. “Spatial-Driven Features Based on Image Dependencies for Person Re-Identification.” Pattern Recognition 124:108462. https://doi.org/10.1016/j.patcog.2021.108462.
  • Si, Tongzhen, Fazhi He, Zhong Zhang, and Yansong Duan. 2022. “Hybrid Contrastive Learning for Unsupervised Person Re-Identification.” IEEE Transactions on Multimedia. https://doi.org/10.1109/TMM.2022.3174414.
  • 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.
  • Song, Lei, Min Xia, Junlan Jin, Ming Qian, and Yonghong Zhang. 2021. “SUACDNet: Attentional Change Detection Network Based on Siamese U-Shaped Structure.” International Journal of Applied Earth Observation and Geoinformation 105:102597. https://doi.org/10.1016/j.jag.2021.102597.
  • Song, Shaoyue, Hongkai Yu, Zhenjiang Miao, Qiang Zhang, Yuewei Lin, and Song Wang. 2019. “Domain Adaptation for Convolutional Neural Networks-Based Remote Sensing Scene Classification.” IEEE Geoscience and Remote Sensing Letters 16 (8): 1324–1328. https://doi.org/10.1109/LGRS.2019.2896411.
  • Sun, Baochen, and Kate Saenko. 2016. “Deep Coral: Correlation Alignment for Deep Domain Adaptation.” Paper presented at the Proceedings of the European Conference on Computer Vision.
  • Taalab, Khaled, Tao Cheng, and Yang Zhang. 2018. “Mapping Landslide Susceptibility and Types Using Random Forest.” Big Earth Data 2 (2): 159–178. https://doi.org/10.1080/20964471.2018.1472392.
  • Toldo, Marco, Andrea Maracani, Umberto Michieli, and Pietro Zanuttigh. 2020. “Unsupervised Domain Adaptation in Semantic Segmentation: A Review.” Technologies 8 (2): 35. https://doi.org/10.3390/technologies8020035.
  • 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. https://doi.org/10.1080/20964471.2022.2043520.
  • Tsai, Yi Hsuan, Wei Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming Hsuan Yang, and Manmohan Chandraker. 2018. “Learning to Adapt Structured Output Space for Semantic Segmentation.” Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Tzeng, Eric, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. 2014. “Deep Domain Confusion: Maximizing for Domain Invariance.” arXiv preprint arXiv:1412.3474.
  • Valanarasu, Jeya Maria Jose, and Vishal M. Patel. 2022. “UNeXt: MLP-Based Rapid Medical Image Segmentation Network.” Paper presented at the Medical Image Computing and Computer Assisted Intervention.
  • Vohora, V. K., and S. L. Donoghue. 2004. “Application of Remote Sensing Data to Landslide Mapping in Hong Kong.” International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences.
  • Wang, Yi, Zhice Fang, and Haoyuan Hong. 2019. “Comparison of Convolutional Neural Networks for Landslide Susceptibility Mapping in Yanshan County, China.” Science of the Total Environment 666:975–993. https://doi.org/10.1016/j.scitotenv.2019.02.263.
  • Wang, Huan, Yijun Li, Yuehai Wang, Haoji Hu, and Ming-Hsuan Yang. 2020. “Collaborative Distillation for Ultra-Resolution Universal Style Transfer.” Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Wang, Haoran, Tong Shen, Wei Zhang, Ling-Yu Duan, and Tao Mei. 2020. “Classes Matter: A Fine-Grained Adversarial Approach to Cross-Domain Semantic Segmentation.” Paper presented at the Proceedings of the European Conference on Computer Vision.
  • Wang, Junjue, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zhong. 2021. “LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation.” arXiv preprint arXiv:2110.08733.
  • Xu, Qingsong, Chaojun Ouyang, Tianhai Jiang, Xin Yuan, Xuanmei Fan, and Duoxiang Cheng. 2022. “MFFENet and ADANet: A Robust Deep Transfer Learning Method and Its Application in High Precision and Fast Cross-Scene Recognition of Earthquake-Induced Landslides.” Landslides 19 (7): 1617–1647. https://doi.org/10.1007/s10346-022-01847-1.
  • Yang, Zhiyuan, Jing Li, Juha Hyyppä, Jianhua Gong, Jingbin Liu, and Banghui Yang. 2023. “A Comprehensive and Up-to-Date Web-Based Interactive 3D Emergency Response and Visualization System Using Cesium Digital Earth: Taking Landslide Disaster as an Example.” Big Earth Data, 1–23. https://doi.org/10.1080/20964471.2023.2172823.
  • Yi, Yaning, and Wanchang 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. https://doi.org/10.1109/JSTARS.2020.3028855.
  • Yu, Bo, Fang Chen, Chong Xu, Lei Wang, and Ning Wang. 2021. “Matrix SegNet: A Practical Deep Learning Framework for Landslide Mapping from Images of Different Areas with Different Spatial Resolutions.” Remote Sensing 13 (16): 3158. https://doi.org/10.3390/rs13163158.
  • Zhang, Yunlong, Changxing Jing, Huangxing Lin, Chaoqi Chen, Yue Huang, Xinghao Ding, and Yang Zou. 2021. “Hard Class Rectification for Domain Adaptation.” Knowledge-Based Systems 222:107011. https://doi.org/10.1016/j.knosys.2021.107011.
  • Zhang, Brian Hu, Blake Lemoine, and Margaret Mitchell. 2018. “Mitigating Unwanted Biases with Adversarial Learning.” Paper presented at the Proceedings of the AAAI Conference on AI, Ethics, and Society.
  • Zhang, Zhong, Yanan Wang, Shuang Liu, Baihua Xiao, and Tariq S. Durrani. 2022. “Cross-Domain Person Re-Identification Using Heterogeneous Convolutional Network.” IEEE Transactions on Circuits and Systems for Video Technology 32 (3): 1160–1171. https://doi.org/10.1109/TCSVT.2021.3074745.
  • Zhu, Jun Yan, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks.” Paper presented at the Proceedings of the IEEE International Conference on Computer Vision.
  • Zou, Yang, Zhiding Yu, B. V. K. Kumar, and Jinsong Wang. 2018. “Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training.” Paper presented at the Proceedings of the European Conference on Computer Vision.