1,804
Views
0
CrossRef citations to date
0
Altmetric
Articles

GMTS: GNN-based multi-scale transformer siamese network for remote sensing building change detection

, & ORCID Icon
Pages 1685-1706 | Received 09 Jan 2023, Accepted 28 Apr 2023, Published online: 09 May 2023

References

  • Bandara, Wele Gedara Chaminda, and Vishal M. Patel. 2022. “A Transformer-Based Siamese Network for Change Detection.” arXiv preprint arXiv:2201.01293.
  • Cao, Jinming, Yangyan Li, Mingchao Sun, Ying Chen, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen, and Changhe Tu. 2022. “Do-Conv: Depthwise Over-Parameterized Convolutional Layer.” IEEE Transactions on Image Processing 31: 3726–3736. doi:10.1109/TIP.2022.3175432.
  • Chen, Hao, Zipeng Qi, and Zhenwei Shi. 2021. “Remote Sensing Image Change Detection with Transformers.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–14. doi:10.1109/TGRS.2020.3034752.
  • Chen, Hao, and Zhenwei Shi. 2020. “A Spatial-Temporal Attention-based Method and a New Dataset for Remote Sensing Image Change Detection.” Remote Sensing 12 (10): 1662. doi:10.3390/rs12101662.
  • Chen, Jie, Ziyang Yuan, Jian Peng, Li Chen, Haozhe Huang, Jiawei Zhu, Yu Liu, and Haifeng Li. 2020. “DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-resolution Satellite Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 1194–1206. doi:10.1109/JSTARS.4609443.
  • Cheng, Hongquan, Huayi Wu, Jie Zheng, Kunlun Qi, and Wenxuan Liu. 2021. “A Hierarchical Self-Attention Augmented Laplacian Pyramid Expanding Network for Change Detection in High-resolution Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 182: 52–66. doi:10.1016/j.isprsjprs.2021.10.001.
  • Daudt, Rodrigo Caye, Bertr Le Saux, and Alexandre Boulch. 2018. “Fully Convolutional Siamese Networks for Change Detection.” In 2018 25th IEEE International Conference on Image Processing (ICIP), 4063–4067. IEEE.
  • De Bem, Pablo Pozzobon, Osmar Abílio de Carvalho Junior, Renato Fontes Guimarães, and Roberto Arnaldo Trancoso Gomes. 2020. “Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks.” Remote Sensing 12 (6): 901. doi:10.3390/rs12060901.
  • Diakogiannis, Foivos I., François Waldner, and Peter Caccetta. 2021. “Looking for Change? Roll the Dice and Demand Attention.” Remote Sensing 13 (18): 3707. doi:10.3390/rs13183707.
  • Elmahdy, Samy Ismail, and Mohamed Mostafa Mohamed. 2018. “Monitoring and Analysing the Emirate of Dubai's Land Use/land Cover Changes: An Integrated, Low-cost Remote Sensing Approach.” International Journal of Digital Earth 11 (11): 1132–1150. doi:10.1080/17538947.2017.1379563.
  • Fang, Sheng, Kaiyu Li, Jinyuan Shao, and Zhe Li. 2021. “SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images.” IEEE Geoscience and Remote Sensing Letters 19: 1–5. doi:10.1109/LGRS.2021.3056416.
  • Gong, Maoguo, Yuelei Yang, Tao Zhan, Xudong Niu, and Shuwei Li. 2019. “A Generative Discriminatory Classified Network for Change Detection in Multispectral Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (1): 321–333. doi:10.1109/JSTARS.4609443.
  • Han, Kai, Yunhe Wang, Jianyuan Guo, Yehui Tang, and Enhua Wu. 2022. “Vision GNN: An Image is Worth Graph of Nodes.” arXiv preprint arXiv:2206.00272.
  • Hendrycks, Dan, and Kevin Gimpel. 2016. “Gaussian Error Linear Units (Gelus).” arXiv preprint arXiv:1606.08415.
  • Iino, Shota, Riho Ito, Kento Doi, Tomoyuki Imaizumi, and Shuhei Hikosaka. 2017. “Generating High-Accuracy Urban Distribution Map for Short-Term Change Monitoring Based on Convolutional Neural Network by Utilizing SAR Imagery.” In Earth Resources and Environmental Remote Sensing/GIS Applications VIII, Vol. 10428, 11–21. SPIE.
  • Ji, Shunping, Shiqing Wei, and Meng Lu. 2018. “Fully Convolutional Networks for Multisource Building Extraction From An Open Aerial and Satellite Imagery Data Set.” IEEE Transactions on Geoscience and Remote Sensing 57 (1): 574–586. doi:10.1109/TGRS.2018.2858817.
  • Jiang, Huiwei, Xiangyun Hu, Kun Li, Jinming Zhang, Jinqi Gong, and Mi Zhang. 2020. “PGA-SiamNet: Pyramid Feature-based Attention-guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection.” Remote Sensing 12 (3): 484. doi:10.3390/rs12030484.
  • Li, Sike. 2018. “Change Detection: How Has Urban Expansion in Buenos Aires Metropolitan Region Affected Croplands.” International Journal of Digital Earth 11 (2): 195–211. doi:10.1080/17538947.2017.1311954.
  • Li, Shujun, and Lianzhi Huo. 2021. “Remote Sensing Image Change Detection Based on Fully Convolutional Network With Pyramid Attention.” In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 4352–4355. IEEE.
  • Liu, Mengxi, Zhuoqun Chai, Haojun Deng, and Rong Liu. 2022. “A CNN-Transformer Network with Multi-scale Context Aggregation for Fine-grained Cropland Change Detection.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15: 4297–4306. doi:10.1109/JSTARS.2022.3177235.
  • Liu, Yi, Chao Pang, Zongqian Zhan, Xiaomeng Zhang, and Xue Yang. 2020. “Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model.” IEEE Geoscience and Remote Sensing Letters 18 (5): 811–815. doi:10.1109/LGRS.2020.2988032.
  • Liu, Meng, Hong Zhang, Chao Wang, Yixian Tang, Bo Zhang, Fan Wu, Tao Wu, and Xi Chen. 2015. “Polarimetric Synthetic Aperture Radar Change Detection for Specific Land Cover Types.” International Journal of Digital Earth 8 (4): 334–344. doi:10.1080/17538947.2013.872702.
  • Mucher, C. A., K. T. Steinnocher, F. P. Kressler, and C Heunks. 2000. “Land Cover Characterization and Change Detection for Environmental Monitoring of Pan-Europe.” International Journal of Remote Sensing 21 (6-7): 1159–1181. doi:10.1080/014311600210128.
  • Pan, Zizheng, Jianfei Cai, and Bohan Zhuang. 2022. “Fast Vision Transformers With HiLo Attention.” arXiv preprint arXiv:2205.13213.
  • Park, Namuk, and Songkuk Kim. 2022. “How Do Vision Transformers Work?” arXiv preprint arXiv:2202.06709.
  • Peng, Daifeng, Lorenzo Bruzzone, Yongjun Zhang, Haiyan Guan, Haiyong Ding, and Xu Huang. 2020. “SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 59 (7): 5891–5906. doi:10.1109/TGRS.2020.3011913.
  • Peng, Bo, Zonglin Meng, Qunying Huang, and Caixia Wang. 2019. “Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-temporal Satellite Multispectral Imagery.” Remote Sensing 11 (21): 2492. doi:10.3390/rs11212492.
  • Peng, Xueli, Ruofei Zhong, Zhen Li, and Qingyang Li. 2020. “Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference.” IEEE Transactions on Geoscience and Remote Sensing 59 (9): 7296–7307. doi:10.1109/TGRS.2020.3033009.
  • Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. 2015. “U-net: Convolutional Networks for Biomedical Image Segmentation.” In International Conference on Medical image computing and computer-assisted intervention, 234–241. Springer.
  • Song, Kaiqiang, Fengzhi Cui, and Jie Jiang. 2021. “An Efficient Lightweight Neural Network for Remote Sensing Image Change Detection.” Remote Sensing 13 (24): 5152. doi:10.3390/rs13245152.
  • Song, Xinyang, Zhen Hua, and Jinjiang Li. 2022a. “PSTNet: Progressive Sampling Transformer Network for Remote Sensing Image Change Detection.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15: 8442–8455. doi:10.1109/JSTARS.2022.3204191.
  • Song, Xinyang, Zhen Hua, and Jinjiang Li. 2022b. “Remote Sensing Image Change Detection Transformer Network Based on Dual-Feature Mixed Attention.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–16. doi:10.1109/TGRS.2022.3209972.
  • Song, Kaiqiang, and Jie Jiang. 2021. “AGCDetNet: An Attention-Guided Network for Building Change Detection in High-resolution Remote Sensing Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 4816–4831. doi:10.1109/JSTARS.2021.3077545.
  • Stylianidis, Efstratios, Devrim Akca, Daniela Poli, Martin Hofer, Armin Gruen, Victor Sanchez Martin, and Konstantinos Smagas, et al. 2020. “FORSAT: A 3D Forest Monitoring System for Cover Mapping and Volumetric 3D Change Detection.” International Journal of Digital Earth 13 (8): 854–885. doi:10.1080/17538947.2019.1585975.
  • Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. “Attention is All You Need.” Advances in Neural Information Processing Systems 30: 6000–6010. doi:10.48550/arXiv.1706.03762.
  • Voigtman, Edward, and James D. Winefordner. 1986. “Low-Pass Filters for Signal Averaging.” Review of Scientific Instruments 57 (5): 957–966. doi:10.1063/1.1138645.
  • Wang, Rongfang, Fan Ding, Jia-Wei Chen, Bo Liu, Jie Zhang, and Licheng Jiao. 2020. “SAR Image Change Detection Method via a Pyramid Pooling Convolutional Neural Network.” In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, 312–315. IEEE.
  • Wang, Xiaolong, Ross Girshick, Abhinav Gupta, and Kaiming He. 2018. “Non-Local Neural Networks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7794–7803.
  • Yang, Zhilin, Zihang Dai, Ruslan Salakhutdinov, and William W. Cohen. 2017. “Breaking the Softmax Bottleneck: A High-Rank RNN Language Model.” arXiv preprint arXiv:1711.03953.
  • Yang, Jianwei, Chunyuan Li, Pengchuan Zhang, Xiyang Dai, Bin Xiao, Lu Yuan, and Jianfeng Gao. 2021. “Focal Self-Attention for Local-Global Interactions in Vision Transformers.” arXiv preprint arXiv:2107.00641.
  • Yuan, Panli, Qingzhan Zhao, Xingbiao Zhao, Xuewen Wang, Xuefeng Long, and Yuchen Zheng. 2022. “A Transformer-based Siamese Network and An Open Optical Dataset for Semantic Change Detection of Remote Sensing Images.” International Journal of Digital Earth 15 (1): 1506–1525. doi:10.1080/17538947.2022.2111470.
  • Zhang, Min, and Wenzhong Shi. 2020. “A Feature Difference Convolutional Neural Network-Based Change Detection Method.” IEEE Transactions on Geoscience and Remote Sensing 58 (10): 7232–7246. doi:10.1109/TGRS.36.
  • Zhang, Cui, Liejun Wang, Shuli Cheng, and Yongming Li. 2022. “SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection.” IEEE Transactions on Geoscience and Remote Sensing60: 1–13. doi:10.1109/TGRS.2022.3160007.
  • Zhang, Yuxiang, Ke Wu, Bo Du, and Xiangyun Hu. 2019. “Multitask Learning-based Reliability Analysis for Hyperspectral Target Detection.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (7): 2135–2147. doi:10.1109/JSTARS.4609443.
  • Zhang, Chenxiao, Peng Yue, Deodato Tapete, Liangcun Jiang, Boyi Shangguan, Li Huang, and Guangchao Liu. 2020. “A Deeply Supervised Image Fusion Network for Change Detection in High Resolution Bi-temporal Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 166: 183–200. doi:10.1016/j.isprsjprs.2020.06.003.
  • Zhao, Wenzhi, Lichao Mou, Jiage Chen, Yanchen Bo, and William J. Emery. 2019. “Incorporating Metric Learning and Adversarial Network for Seasonal Invariant Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 58 (4): 2720–2731. doi:10.1109/TGRS.36.
  • Zhou, Yuan, and Xiangrui Li. 2020. “Unsupervised Self-Training Algorithm Based on Deep Learning for Optical Aerial Images Change Detection.” arXiv preprint arXiv:2010.07469.