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BD-MSA: Body decouple VHR Remote Sensing Image Change Detection method guided by multi-scale feature information aggregation (2401.04330v2)

Published 9 Jan 2024 in cs.CV and cs.AI

Abstract: The purpose of remote sensing image change detection (RSCD) is to detect differences between bi-temporal images taken at the same place. Deep learning has been extensively used to RSCD tasks, yielding significant results in terms of result recognition. However, due to the shooting angle of the satellite, the impacts of thin clouds, and certain lighting conditions, the problem of fuzzy edges in the change region in some remote sensing photographs cannot be properly handled using current RSCD algorithms. To solve this issue, we proposed a Body Decouple Multi-Scale by fearure Aggregation change detection (BD-MSA), a novel model that collects both global and local feature map information in the channel and space dimensions of the feature map during the training and prediction phases. This approach allows us to successfully extract the change region's boundary information while also divorcing the change region's main body from its boundary. Numerous studies have shown that the assessment metrics and evaluation effects of the model described in this paper on the publicly available datasets DSIFN-CD, S2Looking and WHU-CD are the best when compared to other models.

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References (56)
  1. A. Singh, “Review article digital change detection techniques using remotely-sensed data,” International journal of remote sensing, vol. 10, no. 6, pp. 989–1003, 1989.
  2. I. Onur, D. Maktav, M. Sari, and N. Kemal Sönmez, “Change detection of land cover and land use using remote sensing and gis: a case study in kemer, turkey,” International Journal of Remote Sensing, vol. 30, no. 7, pp. 1749–1757, 2009.
  3. R. E. Kennedy, P. A. Townsend, J. E. Gross, W. B. Cohen, P. Bolstad, Y. Wang, and P. Adams, “Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects,” Remote sensing of environment, vol. 113, no. 7, pp. 1382–1396, 2009.
  4. P. Du, S. Liu, P. Gamba, K. Tan, and J. Xia, “Fusion of difference images for change detection over urban areas,” IEEE journal of selected topics in applied earth observations and remote sensing, vol. 5, no. 4, pp. 1076–1086, 2012.
  5. Z. Zheng, Y. Zhong, J. Wang, A. Ma, and L. Zhang, “Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters,” Remote Sensing of Environment, vol. 265, p. 112636, 2021.
  6. P. R. Coppin and M. E. Bauer, “Digital change detection in forest ecosystems with remote sensing imagery,” Remote sensing reviews, vol. 13, no. 3-4, pp. 207–234, 1996.
  7. J. Deng, K. Wang, Y. Deng, and G. Qi, “Pca-based land-use change detection and analysis using multitemporal and multisensor satellite data,” International Journal of Remote Sensing, vol. 29, no. 16, pp. 4823–4838, 2008.
  8. C. He, A. Wei, P. Shi, Q. Zhang, and Y. Zhao, “Detecting land-use/land-cover change in rural–urban fringe areas using extended change-vector analysis,” International Journal of Applied Earth Observation and Geoinformation, vol. 13, no. 4, pp. 572–585, 2011.
  9. C. Wu, B. Du, and L. Zhang, “Slow feature analysis for change detection in multispectral imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 5, pp. 2858–2874, 2013.
  10. 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.
  11. D. Peng, Y. Zhang, and H. Guan, “End-to-end change detection for high resolution satellite images using improved unet++,” Remote Sensing, vol. 11, no. 11, p. 1382, 2019.
  12. C. Zhang, P. Yue, D. Tapete, L. Jiang, B. Shangguan, L. Huang, and G. Liu, “A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 166, pp. 183–200, 2020.
  13. H. Chen, W. Li, and Z. Shi, “Adversarial instance augmentation for building change detection in remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–16, 2021.
  14. Y. Liu, C. Pang, Z. Zhan, X. Zhang, and X. Yang, “Building change detection for remote sensing images using a dual-task constrained deep siamese convolutional network model,” IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 5, pp. 811–815, 2020.
  15. A. Codegoni, G. Lombardi, and A. Ferrari, “Tinycd: a (not so) deep learning model for change detection,” Neural Computing and Applications, vol. 35, no. 11, pp. 8471–8486, 2023.
  16. Q. Guo, J. Zhang, S. Zhu, C. Zhong, and Y. Zhang, “Deep multiscale siamese network with parallel convolutional structure and self-attention for change detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–12, 2021.
  17. 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.
  18. C. Han, C. Wu, H. Guo, M. Hu, and H. Chen, “Hanet: A hierarchical attention network for change detection with bi-temporal very-high-resolution remote sensing images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023.
  19. 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.
  20. 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.
  21. D. Wang, X. Chen, M. Jiang, S. Du, B. Xu, and J. Wang, “Ads-net: An attention-based deeply supervised network for remote sensing image change detection,” International Journal of Applied Earth Observation and Geoinformation, vol. 101, p. 102348, 2021.
  22. Z. Li, C. Yan, Y. Sun, and Q. Xin, “A densely attentive refinement network for change detection based on very-high-resolution bitemporal remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–18, 2022.
  23. M. Liu, Q. Shi, A. Marinoni, D. He, X. Liu, and L. Zhang, “Super-resolution-based change detection network with stacked attention module for images with different resolutions,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–18, 2021.
  24. Z. Li, C. Tang, L. Wang, and A. Y. Zomaya, “Remote sensing change detection via temporal feature interaction and guided refinement,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–11, 2022.
  25. 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.
  26. 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.
  27. D. Wang, J. Zhang, B. Du, G.-S. Xia, and D. Tao, “An empirical study of remote sensing pretraining,” IEEE Transactions on Geoscience and Remote Sensing, 2022.
  28. C. Zhang, L. Wang, S. Cheng, and Y. Li, “Swinsunet: Pure transformer network for remote sensing image change detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–13, 2022.
  29. W. Wang, X. Tan, P. Zhang, and X. Wang, “A cbam based multiscale transformer fusion approach for remote sensing image change detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 6817–6825, 2022.
  30. Q. Li, R. Zhong, X. Du, and Y. Du, “Transunetcd: A hybrid transformer network for change detection in optical remote-sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–19, 2022.
  31. X. Song, Z. Hua, and J. Li, “Remote sensing image change detection transformer network based on dual-feature mixed attention,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–16, 2022.
  32. T. Yan, Z. Wan, and P. Zhang, “Fully transformer network for change detection of remote sensing images,” in Proceedings of the Asian Conference on Computer Vision, 2022, pp. 1691–1708.
  33. W. Liu, Y. Lin, W. Liu, Y. Yu, and J. Li, “An attention-based multiscale transformer network for remote sensing image change detection,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 202, pp. 599–609, 2023.
  34. Q. Ke and P. Zhang, “Hybrid-transcd: A hybrid transformer remote sensing image change detection network via token aggregation,” ISPRS International Journal of Geo-Information, vol. 11, no. 4, p. 263, 2022.
  35. S. S. Islam, S. Rahman, M. M. Rahman, E. K. Dey, and M. Shoyaib, “Application of deep learning to computer vision: A comprehensive study,” in 2016 5th international conference on informatics, electronics and vision (ICIEV).   IEEE, 2016, pp. 592–597.
  36. E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Convolutional neural networks for large-scale remote-sensing image classification,” IEEE Transactions on geoscience and remote sensing, vol. 55, no. 2, pp. 645–657, 2016.
  37. G. Cheng and J. Han, “A survey on object detection in optical remote sensing images,” ISPRS journal of photogrammetry and remote sensing, vol. 117, pp. 11–28, 2016.
  38. Z. Deng, H. Sun, S. Zhou, J. Zhao, L. Lei, and H. Zou, “Multi-scale object detection in remote sensing imagery with convolutional neural networks,” ISPRS journal of photogrammetry and remote sensing, vol. 145, pp. 3–22, 2018.
  39. R. Kemker, C. Salvaggio, and C. Kanan, “Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning,” ISPRS journal of photogrammetry and remote sensing, vol. 145, pp. 60–77, 2018.
  40. X. Yuan, J. Shi, and L. Gu, “A review of deep learning methods for semantic segmentation of remote sensing imagery,” Expert Systems with Applications, vol. 169, p. 114417, 2021.
  41. R. Zhang, H. Zhang, X. Ning, X. Huang, J. Wang, and W. Cui, “Global-aware siamese network for change detection on remote sensing images,” ISPRS journal of photogrammetry and remote sensing, 2023.
  42. Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: A nested u-net architecture for medical image segmentation,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4.   Springer, 2018, pp. 3–11.
  43. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
  44. S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “Cbam: Convolutional block attention module,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 3–19.
  45. J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7132–7141.
  46. Y. Li, T. Yao, Y. Pan, and T. Mei, “Contextual transformer networks for visual recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 2, pp. 1489–1500, 2022.
  47. E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in Neural Information Processing Systems, vol. 34, pp. 12 077–12 090, 2021.
  48. D. Hendrycks and K. Gimpel, “Gaussian error linear units (gelus),” arXiv preprint arXiv:1606.08415, 2016.
  49. S. Fang, K. Li, and Z. Li, “Changer: Feature interaction is what you need for change detection,” IEEE Transactions on Geoscience and Remote Sensing, 2023.
  50. X. Huang and S. Belongie, “Arbitrary style transfer in real-time with adaptive instance normalization,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 1501–1510.
  51. Y.-H. Tsai, M.-H. Yang, and M. J. Black, “Video segmentation via object flow,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 3899–3908.
  52. L. Shen, Y. Lu, H. Chen, H. Wei, D. Xie, J. Yue, R. Chen, S. Lv, and B. Jiang, “S2looking: A satellite side-looking dataset for building change detection,” Remote Sensing, vol. 13, no. 24, p. 5094, 2021.
  53. M. Contributors, “MMCV: OpenMMLab computer vision foundation,” https://github.com/open-mmlab/mmcv, 2018.
  54. J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431–3440.
  55. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18.   Springer, 2015, pp. 234–241.
  56. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 618–626.
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