Scale-wised feature enhancement network for change captioning of remote sensing images

References

• Bandara, W. G. C., and V. M. Patel. 2022. “A Transformer-Based Siamese Network for Change Detection.” In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 207–210. IEEE. July.
   Google Scholar
• Banerjee, S., and A. Lavie. 2005. “METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments.” In Proceedings of the acl Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, Ann Arbor, Michigan, 65–72. June.
   Google Scholar
• Chang, S., and P. Ghamisi. 2023. “Changes to Captions: An Attentive Network for Remote Sensing Change Captioning.” IEEE Transactions on Image Processing 32:6047–6060. https://doi.org/10.1109/TIP.2023.3328224.
   PubMedGoogle Scholar
• Chen, C. F. R., Q. Fan, and R. Panda. 2021. Crossvit: Cross-attention multi-scale vision transformer for image classification. In Proceedings of the IEEE/CVF international conference on computer vision, Montreal, QC, Canada, 357–366.
   Google Scholar
• Chen, J., Y. Han, L. Wan, X. Zhou, and M. Deng. 2019. “Geospatial Relation Captioning for High-Spatial-Resolution Images by Using an Attention-Based Neural Network.” International Journal of Remote Sensing 40 (16): 6482–6498. https://doi.org/10.1080/01431161.2019.1594439.
   Web of Science ®Google Scholar
• Chen, L. C., G. Papandreou, F. Schroff, and H. Adam. 2017. “Rethinking Atrous Convolution for Semantic Image Segmentation.” ArXiv Preprint arXiv:1706.05587.
   Google Scholar
• Chouaf, S., G. Hoxha, Y. Smara, and F. Melgani. 2021. “Captioning Changes in Bi-Temporal Remote Sensing Images.” In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2891–2894. IEEE. July.
   Google Scholar
• Chung, J., C. Gulcehre, K. Cho, and Y. Bengio. 2014. “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.” arXiv preprint arXiv:1412.3555.
   Google Scholar
• Cui, F., and J. Jiang. 2023. “MTSCD-Net: A Network Based on Multi-Task Learning for Semantic Change Detection of Bitemporal Remote Sensing Images.” International Journal of Applied Earth Observation and Geoinformation 118:103294. https://doi.org/10.1016/j.jag.2023.103294.
   Google Scholar
• Daudt, R. C., B. Le Saux, and A. Boulch. 2018. “Fully Convolutional Siamese Networks for Change Detection.” 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 4063–4067. IEEE. October.
   Google Scholar
• Daudt, R. C., B. Le Saux, A. Boulch, and Y. Gousseau. 2019. “Multitask Learning for Large-Scale Semantic Change Detection.” Computer Vision and Image Understanding 187:102783. https://doi.org/10.1016/j.cviu.2019.07.003.
   Web of Science ®Google Scholar
• Ding, L., H. Guo, S. Liu, L. Mou, J. Zhang, and L. Bruzzone. 2022. “Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images.” IEEE Transactions on Geoscience & Remote Sensing 60:1–14. https://doi.org/10.1109/TGRS.2022.3154390.
   Google Scholar
• Fang, S., K. Li, J. Shao, and Z. Li. 2021. “SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images.” IEEE Geoscience & Remote Sensing Letters 19:1–5. https://doi.org/10.1109/LGRS.2021.3056416.
   Web of Science ®Google Scholar
• Ganesan, K. 2018. “Rouge 2.0: Updated and improved measures for evaluation of summarization tasks.” arXiv preprint arXiv:1803.01937.
   Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2015. “Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.” IEEE Transactions on Pattern Analysis & Machine Intelligence 37 (9): 1904–1916. https://doi.org/10.1109/TPAMI.2015.2389824.
   PubMed Web of Science ®Google Scholar
• Hochreiter, S. 1998. “The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions.” International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems 6 (2): 107–116. https://doi.org/10.1142/S0218488598000094.
   Web of Science ®Google Scholar
• Hoxha, G., S. Chouaf, F. Melgani, and Y. Smara. 2022. “Change Captioning: A New Paradigm for Multitemporal Remote Sensing Image Analysis.” IEEE Transactions on Geoscience & Remote Sensing 60:1–14. https://doi.org/10.1109/TGRS.2022.3195692.
   Google Scholar
• Jamali, A., and M. Mahdianpari. 2022. “Swin Transformer and Deep Convolutional Neural Networks for Coastal Wetland Classification Using Sentinel-1, Sentinel-2, and LiDAR Data.” Remote Sensing 14 (2): 359. https://doi.org/10.3390/rs14020359.
   Web of Science ®Google Scholar
• LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. “Gradient-Based Learning Applied to Document Recognition.” Proceedings of the IEEE 86 (11): 2278–2324. https://doi.org/10.1109/5.726791.
   Web of Science ®Google Scholar
• Lin, T. Y., P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie. 2017. “Feature Pyramid Networks for Object Detection.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, 2117–2125.
   Google Scholar
• Liu, C., J. Yang, Z. Qi, Z. Zou, and Z. Shi. 2023. “Progressive scale-aware network for remote sensing image change captioning.” In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 6668–6671. IEEE.
   Google Scholar
• Liu, C., R. Zhao, H. Chen, Z. Zou, and Z. Shi. 2022. “Remote Sensing Image Change Captioning with Dual-Branch Transformers: A New Method and a Large Scale Dataset.” IEEE Transactions on Geoscience & Remote Sensing 60:1–20. https://doi.org/10.1109/TGRS.2022.3218921.
   Google Scholar
• Liu, M., Q. Shi, A. Marinoni, D. He, X. Liu, and L. Zhang. 2021. “Super-Resolution-Based Change Detection Network with Stacked Attention Module for Images with Different Resolutions.” IEEE Transactions on Geoscience & Remote Sensing 60:1–18. https://doi.org/10.1109/TGRS.2021.3091758.
   Web of Science ®Google Scholar
• Liu, R., F. Tao, X. Liu, J. Na, H. Leng, J. Wu, and T. Zhou. 2022. “RAANet: A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images.” Remote Sensing 14 (13): 3109. https://doi.org/10.3390/rs14133109.
   Web of Science ®Google Scholar
• Liu, S., and D. Huang. 2018. “Receptive Field Block Net for Accurate and Fast Object Detection.” Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 385–400.
   Google Scholar
• Liu, Z., Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, and B. Guo. 2021. “Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows.” Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 10012–10022.
   Google Scholar
• Papineni, K., S. Roukos, T. Ward, and W. J. Zhu. 2002. “Bleu: A Method for Automatic Evaluation of Machine Translation.” Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia Pennsylvania, 311–318. July.
   Google Scholar
• Ramachandram, D., and G. W. Taylor. 2017. “Deep Multimodal Learning: A Survey on Recent Advances and Trends.” IEEE Signal Processing Magazine 34 (6): 96–108. https://doi.org/10.1109/MSP.2017.2738401.
   Web of Science ®Google Scholar
• Sengupta, A., Y. Ye, R. Wang, C. Liu, and K. Roy. 2019. “Going deeper in spiking neural networks: VGG and residual architectures.” Frontiers in neuroscience 13:95.
   PubMed Web of Science ®Google Scholar
• Su, Y. C., T. J. Liu, and K. H. Liuy. 2022. “Multi-Scale Wavelet Frequency Channel Attention for Remote Sensing Image Segmentation.” 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), Nafplio, Greece, 1–5. IEEE. June.
   Google Scholar
• Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, and A. Rabinovich. 2015. “Going Deeper with Convolutions.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 1–9.
   Google Scholar
• Vedantam, R., C. Lawrence Zitnick, and D. Parikh. 2015. “Cider: Consensus-Based Image Description Evaluation.” Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA, 4566–4575.
   Google Scholar
• Woo, S., J. Park, J. Y. Lee, and I. S. Kweon. 2018. “Cbam: Convolutional Block Attention Module.” Proceedings of the European conference on computer vision (ECCV), Munich, Germany, 3–19.
   Google Scholar
• Wu, Y., K. Zhang, J. Wang, Y. Wang, Q. Wang, and X. Li. 2022. “GCWNet: A Global Context-Weaving Network for Object Detection in Remote Sensing Images.” IEEE Transactions on Geoscience & Remote Sensing 60:1–12. https://doi.org/10.1109/TGRS.2022.3228927.
   Google Scholar
• Xia, H., Y. Tian, L. Zhang, and S. Li. 2022. “A Deep Siamese Postclassification Fusion Network for Semantic Change Detection.” IEEE Transactions on Geoscience & Remote Sensing 60:1–16. https://doi.org/10.1109/TGRS.2022.3171067.
   Web of Science ®Google Scholar
• Yang, S., L. Tian, B. Zhou, D. Chen, D. Zhang, Z. Xu, and J. Liu. 2020. “Inception Parallel Attention Network for Small Object Detection in Remote Sensing Images.” Chinese Conference on Pattern Recognition and Computer Vision (PRCV), 469–480. Cham. Springer International Publishing. October.
   Google Scholar
• Ye, W., W. Zhang, W. Lei, W. Zhang, X. Chen, and Y. Wang. 2023. “Remote Sensing Image Instance Segmentation Network with Transformer and Multi-Scale Feature Representation.” Expert Systems with Applications 234:121007. https://doi.org/10.1016/j.eswa.2023.121007.
   Google Scholar
• Yu, G., and X. Zhang. 2021. “Land Cover Classification Based on PSPNet Using Remote Sensing Image.” 2021 40th Chinese Control Conference (CCC), Shanghai, China, 7349–7354. IEEE. July.
   Google Scholar
• Zhong, X., O. Gong, W. Huang, L. Li, and H. Xia. 2019. “Squeeze-and-excitation wide residual networks in image classification.” In 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 395–399. IEEE.
   Google Scholar
• Zhu, X., S. Lyu, X. Wang, and Q. Zhao. 2021. “TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios.” Proceedings of the IEEE/CVF international conference on computer vision, Montreal, BC, Canada, 2778–2788.
   Google Scholar