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Recent advancements in Remote Sensing (RS) for Change Detection (CD) and Change Captioning (CC) have seen substantial success by adopting deep learning techniques.
Y. Qiu, S. Yamamoto, K. Nakashima, R. Suzuki, K. Iwata, H. Kataoka, and Y. Satoh, “Describing and localizing multiple changes with transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 1971–1980
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C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text summarization branches out , 2004, pp. 74–81
2004
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S. Banerjee and A. Lavie, “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 , 2005, pp. 65–72
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R. Vedantam, C. Lawrence Zitnick, and D. Parikh, “Cider: Consensus-based image description evaluation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 4566–4575
2015
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Z. Liu, G. Li, G. Mercier, Y. He, and Q. Pan, “Change detection in heterogenous remote sensing images via homogeneous pixel transformation,” IEEE Transactions on Image Processing , vol. 27, no. 4, pp. 1822–1834, 2017
2017
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R. C. Daudt, B. Le Saux, and A. Boulch, “Fully convolutional siamese networks for change detection,” in 2018 25th IEEE international conference on image processing (ICIP) . IEEE, 2018, pp. 4063–4067
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S. Elfwing, E. Uchibe, and K. Doya, “Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,” Neural networks , vol. 107, pp. 3–11, 2018
2018
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T. Xiao, Y. Liu, B. Zhou, Y. Jiang, and J. Sun, “Unified perceptual parsing for scene understanding,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 418–434
2018
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P. Ghamisi, B. Rasti, N. Yokoya, Q. Wang, B. Hofle, L. Bruzzone, F. Bovolo, M. Chi, K. Anders, R. Gloaguen, P. M. Atkinson, and J. A. Benediktsson, “Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art,” IEEE Geoscience and Remote Sensing Magazine , vol. 7, no. 1, pp. 6–39, 2019
2019
Earlier work this paper cites.
D. H. Park, T. Darrell, and A. Rohrbach, “Robust change captioning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 4624–4633
2019
Earlier work this paper cites.
Y. Lei, D. Peng, P. Zhang, Q. Ke, and H. Li, “Hierarchical paired channel fusion network for street scene change detection,” IEEE Transactions on Image Processing , vol. 30, pp. 55–67, 2020
2020
Earlier work this paper cites.
H. Chen and Z. Shi, “A spatial-temporal attention-based method and a new dataset for remote sensing image change detection,” Remote Sensing , vol. 12, no. 10, p. 1662, 2020
2020
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2020
2020
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S. Chouaf, G. Hoxha, Y. Smara, and F. Melgani, “Captioning changes in bi-temporal remote sensing images,” in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS . IEEE, 2021, pp. 2891–2894
2021
Earlier work this paper cites.
R. Zhao, Z. Shi, and Z. Zou, “High-resolution remote sensing image captioning based on structured attention,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–14, 2021
2021
Earlier work this paper cites.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 10 012–10 022
2021
Cited alongside, same era.
Q. Shi, M. Liu, S. Li, X. Liu, F. Wang, and L. Zhang, “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 , vol. 60, pp. 1–16, 2021
2021
Cited alongside, same era.
H. Chen, Z. Qi, and Z. Shi, “Remote sensing image change detection with transformers,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–14, 2021
2021
Cited alongside, same era.
S. Fang, K. Li, J. Shao, and Z. Li, “Snunet-cd: A densely connected siamese network for change detection of vhr images,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2021
2021
Cited alongside, same era.
S. Chang and P. Ghamisi, “Changes to captions: An attentive network for remote sensing change captioning,” IEEE Transactions on Image Processing , 2023
2023
Later among the works it cites.
C. Liu, R. Zhao, J. Chen, Z. Qi, Z. Zou, and Z. Shi, “A decoupling paradigm with prompt learning for remote sensing image change captioning,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Later among the works it cites.
Y. Xu, W. Yu, P. Ghamisi, M. Kopp, and S. Hochreiter, “Txt2img-mhn: Remote sensing image generation from text using modern hopfield networks,” IEEE Transactions on Image Processing , 2023
2023
Later among the works it cites.
C. Liu, J. Yang, Z. Qi, Z. Zou, and Z. Shi, “Progressive scale-aware network for remote sensing image change captioning,” in IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2023, pp. 6668–6671
2023
Later among the works it cites.
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Z. Li, F. Lu, H. Zhang, L. Tu, J. Li, X. Huang, C. Robinson, N. Malkin, N. Jojic, P. Ghamisi, R. Hänsch, and N. Yokoya, “The outcome of the 2021 ieee grss data fusion contest—track msd: Multitemporal semantic change detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 15, pp. 1643–1655, 2022
2022
Cited alongside, same era.
X. Zhang, W. Yu, and M.-O. Pun, “Multilevel deformable attention-aggregated networks for change detection in bitemporal remote sensing imagery,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–18, 2022
2022
Cited alongside, same era.
C. Liu, R. Zhao, H. Chen, Z. Zou, and Z. Shi, “Remote sensing image change captioning with dual-branch transformers: A new method and a large scale dataset,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–20, 2022
2022
Cited alongside, same era.
G. Hoxha, S. Chouaf, F. Melgani, and Y. Smara, “Change captioning: A new paradigm for multitemporal remote sensing image analysis,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–14, 2022
2022
Cited alongside, same era.
Y. He, X. Feng, C. Cheng, G. Ji, Y. Guo, and J. Caverlee, “Metabalance: improving multi-task recommendations via adapting gradient magnitudes of auxiliary tasks,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 2205–2215
2022
Cited alongside, same era.
M. Liu, Z. Chai, H. Deng, and R. Liu, “A cnn-transformer network with multiscale context aggregation for fine-grained cropland change detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 15, pp. 4297–4306, 2022
2022
Cited alongside, same era.
W. G. C. Bandara and V. M. Patel, “A transformer-based siamese network for change detection,” in IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2022, pp. 207–210
2022
Cited alongside, same era.
Y. Feng, H. Xu, J. Jiang, H. Liu, and J. Zheng, “Icif-net: Intra-scale cross-interaction and inter-scale feature fusion network for bitemporal remote sensing images change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–13, 2022
2022
Cited alongside, same era.
S. Holail, T. Saleh, X. Xiao, and D. Li, “Afde-net: Building change detection using attention-based feature differential enhancement for satellite imagery,” IEEE Geoscience and Remote Sensing Letters , 2023
2023
Later among the works it cites.
Y. Feng, J. Jiang, H. Xu, and J. Zheng, “Change detection on remote sensing images using dual-branch multilevel intertemporal network,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–15, 2023
2023
Later among the works it cites.
2024
Closest in time.
W. Yu, X. Zhang, S. Das, X. Xiang Zhu, and P. Ghamisi, “Maskcd: A remote sensing change detection network based on mask classification,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–16, 2024
2024
Closest in time.
H. Chang, P. Wang, W. Diao, G. Xu, and X. Sun, “Remote sensing change detection with bitemporal and differential feature interactive perception,” IEEE Transactions on Image Processing , 2024
2024
Closest in time.
C. Liu, K. Chen, H. Zhang, Z. Qi, Z. Zou, and Z. Shi, “Change-agent: Towards interactive comprehensive remote sensing change interpretation and analysis,” IEEE Transactions on Geoscience and Remote Sensing , 2024
2024
Closest in time.
C. Wu, L. Zhang, B. Du, H. Chen, J. Wang, and H. Zhong, “Unet-like remote sensing change detection: A review of current models and research directions,” IEEE Geoscience and Remote Sensing Magazine , pp. 2–31, 2024
2024
Closest in time.
H. Chen, J. Song, C. Han, J. Xia, and N. Yokoya, “Changemamba: Remote sensing change detection with spatiotemporal state space model,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–20, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Y. Wang and P. Ghamisi, “Rsadapter: Adapting multimodal models for remote sensing visual question answering,” IEEE Transactions on Geoscience and Remote Sensing , 2024
2024
Closest in time.