HMLNet: a hierarchical metric learning network with dual attention for change detection in high-resolution remote sensing images

References

• Chen, H., and Z. Shi. 2020. “A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection.” Remote Sensing 12 (10): 1662–1684. doi:10.3390/rs12101662.
   Web of Science ®Google Scholar
• Chen, J., Z. Yuan, J. Peng, L. Chen, H. Huang, J. Zhu, Y. Liu, and H. Li. 2021. “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.2020.3037893.
   Web of Science ®Google Scholar
• Cong, R., Y. Zhang, L. Fang, J. Li, C. Zhang, Y. Zhao, and S. Kwong. 2022. “RRNet: Relational Reasoning Network with Parallel Multiscale Attention for Salient Object Detection in Optical Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–11. doi:10.1109/TGRS.2021.3123984.
   Web of Science ®Google Scholar
• Daudt, R. C., B. L. Saux, and A. Boulch. 2018. “Fully Convolutional Siamese Networks for Change Detection.” In Proc. IEEE Int. Conf. Image Process, Athens, Greece. ICIP, 4063–4067.
   Google Scholar
• Diakogiannis, F. I., F. Waldner, P. Caccetta, and C. Wu. 2020. “ResUnet-A: A Deep Learning Framework for Semantic Segmentation of Remotely Sensed Data.” Isprs Journal of Photogrammetry and Remote Sensing 162: 94–114. doi:10.1016/j.isprsjprs.2020.01.013.
   Web of Science ®Google Scholar
• Ding, Q., Z. Shao, X. Huang, and O. Altan. 2021. “DSA-Net: A Novel Deeply Supervised Attention-Guided Network for Building Change Detection in High-Resolution Remote Sensing Images.” International Journal of Applied Earth Observation and Geoinformation 105: 102591–102602. doi:10.1016/j.jag.2021.102591.
   Web of Science ®Google Scholar
• Dong, J., W. Zhao, and S. Wang. 2022. “Multiscale Context Aggregation Network for Building Change Detection Using High Resolution Remote Sensing Images.” IEEE Geoscience and Remote Sensing Letters 19: 1–5. doi:10.1109/LGRS.2021.3121094.
   Web of Science ®Google Scholar
• Farahani, M., and A. Mohammadzadeh. 2020. “Domain Adaptation for Unsupervised Change Detection of Multisensor Multitemporal Remote-Sensing Images.” International Journal of Remote Sensing 41 (10): 3902–3923. doi:10.1080/01431161.2019.1711239.
   Web of Science ®Google Scholar
• Fekete, A., and M. Cserep. 2021. “Tree Segmentation and Change Detection of Large Urban Areas Based on Airborne LiDar.” Computers & Geosciences 156: 104900–104910. doi:10.1016/j.cageo.2021.104900.
   Web of Science ®Google Scholar
• Gao, S. H., M. M. Cheng, K. Zhao, X. Y. Zhang, M. H. Yang, and P. Torr. 2021. “Res2net: A New Multi-Scale Backbone Architecture.” IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (2): 652–662. doi:10.1109/TPAMI.2019.2938758.
   PubMed Web of Science ®Google Scholar
• Gong, M., F. Jiang, A. K. Qin, T. Liu, T. Zhan, D. Lu, H. Zheng, and M. Zhang. 2022. “A Spectral and Spatial Attention Network for Change Detection in Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–14. doi:10.1109/TGRS.2021.3139077.
   Web of Science ®Google Scholar
• Gong, M., H. Yang, and P. Zhang. 2017. “Feature Learning and Change Feature Classification Based on Deep Learning for Ternary Change Detection in SAR Images.” Isprs Journal of Photogrammetry and Remote Sensing 129: 212–225. doi:10.1016/j.isprsjprs.2017.05.001.
   Web of Science ®Google Scholar
• Hao, Y., Z. Chen, Q. Huang, F. Li, B. Wang, and L. Ma. 2020. “Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series.” Remote Sensing 12 (3): 478–504. doi:10.3390/rs12030478.
   Web of Science ®Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” In Proc. IEEE Conf. Comput. Vis. Pattern Recognit, Las Vegas, Nevada, USA, CVPR, 770–778.
   Google Scholar
• Jiang, H., M. Peng, Y. Zhong, H. Xie, Z. Hao, J. Lin, X. Ma, and X. Hu. 2022. “A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images.” Remote Sensing 14 (7): 1552–1582. doi:10.3390/rs14071552.
   Web of Science ®Google Scholar
• Khelifi, L., and M. Mignotte. 2020. “Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis.” IEEE Access 8: 126385–126400. doi:10.1109/ACCESS.2020.3008036.
   Google Scholar
• Liu, G., Y. Gousseau, and F. Tupin. 2019. “A Contrario Comparison of Local Descriptors for Change Detection in Very High Spatial Resolution Satellite Images of Urban Areas.” IEEE Transactions on Geoscience and Remote Sensing 57 (6): 3904–3918. doi:10.1109/TGRS.2018.2888985.
   Web of Science ®Google Scholar
• Liu, G., L. Li, L. Jiao, Y. Dong, and X. Li. 2019. “Stacked Fisher Autoencoder for SAR Change Detection.” Pattern recognition 96: 106971–106982. doi:10.1016/j.patcog.2019.106971.
   Web of Science ®Google Scholar
• Luo, B., C. Hu, X. Su, and Y. Wang. 2019. “Differentially Deep Subspace Representation for Unsupervised Change Detection of SAR Images.” Remote Sensing 11 (23): 2740–2757. doi:10.3390/rs11232740.
   Web of Science ®Google Scholar
• Meiqi, H., W. Chen, L. Zhang, and B. Du. 2021. “Hyperspectral Anomaly Change Detection Based on Autoencoder.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 3750–3762. doi:10.1109/JSTARS.2021.3066508.
   Web of Science ®Google Scholar
• Meyer, B. J., B. Harwood, and T. Drummond. 2018. “Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels.” In Proc. IEEE Int. Conf. Image Process, Athens, Greece, ICIP, 151–155.
   Google Scholar
• Mohsenifar, A., A. Mohammadzadeh, A. Moghimi, and B. Salehi. 2021. “A Novel Unsupervised Forest Change Detection Method Based on the Integration of a Multiresolution Singular Value Decomposition Fusion and an Edge-Aware Markov Random Field Algorithm.” International Journal of Remote Sensing 42 (24): 9376–9404. doi:10.1080/01431161.2021.1995075.
   Web of Science ®Google Scholar
• Peng, D., L. Bruzzone, Y. Zhang, H. Guan, and H. Pengfei. 2021. “SCDNET: A Novel Convolutional Network for Semantic Change Detection in High Resolution Optical Remote Sensing Imagery.” International Journal of Applied Earth Observation and Geoinformation 103: 102465–102480. doi:10.1016/j.jag.2021.102465.
   Web of Science ®Google Scholar
• Peng, D., Y. Zhang, and H. Guan. 2019. “End-To-End Change Detection for High Resolution Satellite Images Using Improved UNet++.” Remote Sensing 11 (11): 1382–1404. doi:10.3390/rs11111382.
   Web of Science ®Google Scholar
• Quanfu, X., K. Chen, G. Zhou, and X. Sun. 2021. “Change Capsule Network for Optical Remote Sensing Image Change Detection.” Remote Sensing 13 (14): 2646–2667. doi:10.3390/rs13142646.
   Web of Science ®Google Scholar
• Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” In Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervention, Munich, Germany, MICCAI, 234–241.
   Google Scholar
• Sefrin, O., F. M. Riese, and S. Keller. 2021. “Deep Learning for Land Cover Change Detection.” Remote Sensing 13 (1): 78–104. doi:10.3390/rs13010078.
   Web of Science ®Google Scholar
• Shen, Q., J. Huang, M. Wang, S. Tao, R. Yang, and X. Zhang. 2022. “Semantic Feature-Constrained Multitask Siamese Network for Building Change Detection in High-Spatial-Resolution Remote Sensing Imagery.” Isprs Journal of Photogrammetry and Remote Sensing 189: 78–94. doi:10.1016/j.isprsjprs.2022.05.001.
   Web of Science ®Google Scholar
• Shi, Q., M. Liu, S. Li, X. Liu, F. Wang, and L. Zhang. 2022. “A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–16. doi:10.1109/TGRS.2021.3085870.
   Web of Science ®Google Scholar
• Shi, W., M. Zhang, R. Zhang, S. Chen, and Z. Zhan. 2020. “Change Detection Based on Artificial Intelligence: State-Of-The-Art and Challenges.” Remote Sensing 12 (10): 1688–1722. doi:10.3390/rs12101688.
   Web of Science ®Google Scholar
• Tang, X., H. Zhang, L. Mou, F. Liu, X. Zhang, X. Zhu, and L. Jiao. 2022. “An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–15. doi:10.1109/TGRS.2021.3106381.
   Web of Science ®Google Scholar
• Wang, R., J. Chen, Y. Wang, L. Jiao, and M. Wang. 2020. “SAR Image Change Detection via Spatial Metric Learning with an Improved Mahalanobis Distance.” IEEE Geoscience and Remote Sensing Letters 17 (1): 77–81. doi:10.1109/LGRS.2019.2915251.
   Web of Science ®Google Scholar
• Wang, Z., C. Peng, Y. Zhang, N. Wang, and L. Luo. 2021. “Fully Convolutional Siamese Networks Based Change Detection for Optical Aerial Images with Focal Contrastive Loss.” Neurocomputing 457: 155–167. doi:10.1016/j.neucom.2021.06.059.
   Web of Science ®Google Scholar
• Woo, S., J. Y. Lee, J. Park, and I. So Kweon. 2018. “CBAM: Convolutional Block Attention Module.” In Proc. Eur. Conf. Comput. Vis. Munich, Germany, ECCV, 3–19.
   Google Scholar
• Xiong, F., Y. Xiao, Z. Cao, Y. Wang, J. T. Zhou, and J. Wu. 2022. “ECML: An Ensemble Cascade Metric-Learning Mechanism Toward Face Verification.” IEEE Transactions on Cybernetics 52 (3): 1736–1749. doi:10.1109/TCYB.2020.2996207.
   PubMed Web of Science ®Google Scholar
• Yang, X., L. Hu, Y. Zhang, and Y. Li. 2021. “MRA-SNet: Siamese Networks of Multiscale Residual and Attention for Change Detection in High-Resolution Remote Sensing Images.” Remote Sensing 13 (22): 4528–4548. doi:10.3390/rs13224528.
   Web of Science ®Google Scholar
• Yang, Y., J. Qu, S. Xiao, W. Dong, Y. Li, and Q. Du. 2022. “A Deep Multiscale Pyramid Network Enhanced with SpatialCspectral Residual Attention for Hyperspectral Image Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–13. doi:10.1109/TGRS.2022.3161386.
   Web of Science ®Google Scholar
• Yang, X., P. Zhou, and M. Wang. 2019. “Person Reidentification via Structural Deep Metric Learning.” IEEE Transactions on Neural Networks and Learning Systems 30 (10): 2987–2998. doi:10.1109/TNNLS.2018.2861991.
   PubMed Web of Science ®Google Scholar
• Zhan, Y., K. Fu, M. Yan, X. Sun, H. Wang, and X. Qiu. 2017. “Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images.” IEEE Geoscience and Remote Sensing Letters 14 (10): 1845–1849. doi:10.1109/LGRS.2017.2738149.
   Web of Science ®Google Scholar
• Zhang, M., G. Xu, K. Chen, M. Yan, and X. Sun. 2019. “Triplet-Based Semantic Relation Learning for Aerial Remote Sensing Image Change Detection.” IEEE Geoscience and Remote Sensing Letters 16 (2): 266–270. doi:10.1109/LGRS.2018.2869608.
   Web of Science ®Google Scholar
• Zhang, X., Y. Yue, W. Gao, S. Yun, S. Qian, H. Yin, and Y. Zhang. 2022. “DifUnet++: A Satellite Images Change Detection Network Based on Unet++ and Differential Pyramid.” IEEE Geoscience and Remote Sensing Letters 19: 1–5. doi:10.1109/LGRS.2021.3049370.
   Web of Science ®Google Scholar
• Zhang, C., P. Yue, D. Tapete, L. Jiang, B. Shangguan, L. Huang, and G. 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.
   Web of Science ®Google Scholar
• Zhao, W., L. Mou, J. Chen, Y. Bo, and W. J. Emery. 2020. “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.2019.2953879.
   Web of Science ®Google Scholar