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Метод обнаружения изменений гиперспектральных изображений с объединенными центральными дифференциальными признаками и спектрально-пространственным вниманием

  • role： First author第一作者
  • Affiliation：

    College of Computer Science, Chongqing University, Chongqing 401331, China

  • Email：zhoutianyuan1016@163.com
  • Introduction：周天媛，研究方向为高光谱变化检测。E-mail： zhoutianyuan1016@163.com
ZHOU Tianyuan1,  
  • Affiliation：

    College of Optoelectronic Engineering, Chongqing University, Chongqing 401331, China

LIU Jiamin2,  
  • Affiliation：

    School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

GUO Tan3,  
  • Affiliation：

    College of Computer Science, Chongqing University, Chongqing 401331, China

FU Chuan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Computer Science, Chongqing University, Chongqing 401331, China

  • Email：luoflyn@163.com
  • Introduction：罗甫林，研究方向为遥感智能解译。E-mail： luoflyn@163.com
LUO Fulin1*

реферат

Многовременные гиперспектральные изображения благодаря богатству спектральных диапазонов и деталей изображения широко применяются в обнаружении изменений. Алгоритмы обнаружения изменений на основе контролируемого обучения часто зависят от большого количества размеченных образцов, что приводит к высоким затратам на разметку. В связи с этим в статье предлагается сеть JCDS²AN (Сеть объединенных центральных разностных признаков и спектрально-пространственного внимания) для обнаружения изменений в гиперспектральных изображениях. Эта сеть способна смягчить колебания признаков изменений при ограниченном количестве образцов, используя ограниченное количество размеченных данных для изучения представительных признаков изменений. JCDS²AN разработана с многомасштабными блоками спектрально-пространственного внимания для захвата многомасштабных пространственных и спектральных признаков, а также с использованием стратегии обмена центральных пикселей, направленной дифференциальными признаками, для эффективного взаимодействия между признаками изменений и признаками двух временных фаз. Проведено сравнение с 8 другими методами обнаружения изменений гиперспектра с визуальными и количественными экспериментами на 3 открытых наборах данных. Результаты подтвердили превосходство предложенной JCDS²AN над другими методами.

ключеви́че слова́

гиперспектральное изображение; дистанционное зондирование; обнаружение изменений; многомасштабные признаки; направленные дифференциальные признаки; центральный пиксель

References

  1. 1.
    Abeywickrama T, Cheema M A and Taniar D. 2016. k-nearest neighbors on road networks: a journey in experimentation and in-memory implementation. Proceedings of the VLDB Endowment, 9(6): 492-503
  2. 2.
    Bovolo F and Bruzzone L. 2007. A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain. IEEE Transactions on Geoscience and Remote Sensing, 45(1): 218-236
  3. 3.
    Celik T. 2009. Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geoscience and Remote Sensing Letters, 6(4): 772-776
  4. 4.
    Chen H R X, Wu C, Du B, Zhang L P and Wang L. 2020. Change detection in multisource VHR images via deep siamese convolutional multiple-layers recurrent neural network. IEEE Transactions on Geoscience and Remote Sensing, 58(4): 2848-2864
  5. 5.
    Chollet F. 2017. Xception: deep learning with depthwise separable convolutions//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE: 1800-1807
  6. 6.
    Cortes C and Vapnik V. 1995. Support-vector networks. Machine Learning, 20(3): 273-297
  7. 7.
    Ding J G, Li X R and Zhao L Y. 2022. CDFormer: a hyperspectral image change detection method based on transformer encoders. IEEE Geoscience and Remote Sensing Letters, 19: 6015405
  8. 8.
    Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X H, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J and Houlsby N. 2021. An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929
  9. 9.
    Gong M G, Jiang F L, Qin A K, Liu T F, Zhan T, Lu D, Zheng H H and Zhang M Y. 2022. A spectral and spatial attention network for change detection in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 60: 5521614
  10. 10.
    Hasanlou M and Seydi S T. 2018. Hyperspectral change detection: an experimental comparative study. International Journal of Remote Sensing, 39(20): 7029-7083
  11. 11.
    Hu M Q, Wu C and Zhang L P. 2024. GlobalMind: global multi-head interactive self-attention network for hyperspectral change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 211: 465-483
  12. 12.
    Hyvärinen A and Oja E. 2000. Independent component analysis: algorithms and applications. Neural Networks, 13(4/5): 411-430
  13. 13.
    Jaturapitpornchai R, Matsuoka M, Kanemoto N, Kuzuoka S, Ito R and Nakamura R. 2019. Newly built construction detection in SAR images using deep learning. Remote Sensing, 11(12): 1444
  14. 14.
    Ji M, Liu L F, Du R L and Buchroithner M F. 2019. A comparative study of texture and convolutional neural network features for detecting collapsed buildings after earthquakes using pre- and post-event satellite imagery. Remote Sensing, 11(10): 1202
  15. 15.
    Jiang H W, Hu X Y, Li K, Zhang J M, Gong J Q and Zhang M. 2020. PGA-SiamNet: pyramid feature-based attention-guided siamese network for remote sensing orthoimagery building change detection. Remote Sensing, 12(3): 484
  16. 16.
    Khan S H, He X M, Porikli F and Bennamoun M. 2017. Forest change detection in incomplete satellite images with deep neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55(9): 5407-5423
  17. 17.
    Khelifi L and Mignotte M. 2020. Deep learning for change detection in remote sensing images: comprehensive review and meta-analysis. IEEE Access, 8: 126385-126400
  18. 18.
    Li F T, Zhou F D, Zhang G, Xiao J F and Zeng P. 2024. HSAA-CD: a hierarchical semantic aggregation mechanism and attention module for non-agricultural change detection in cultivated land. Remote Sensing, 16(8): 1372
  19. 19.
    Lin Y, Li S T, Fang L Y and Ghamisi P. 2020. Multispectral change detection with bilinear convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 17(10): 1757-1761
  20. 20.
    Liu R, Lehman J, Molino P, Such F P, Frank E, Sergeev A and Yosinski J. 2018. An intriguing failing of convolutional neural networks and the CoordConv solution//Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montréal: Curran Associates Inc.: 9628-9639
  21. 21.
    Qu J H, Hou S X, Dong W Q, Li Y S, Xie W Y. 2022. A Multilevel Encoder-Decoder Attention Network for Change Detection in Hyperspectral Images. IEEE Transactions on Geoscience and Remote Sensing, 60:5518113
  22. 22.
    Luo F L, Zhou T Y, Liu J M, Guo T, Gong X W and Ren J C. 2023. Multiscale diff-changed feature fusion network for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing, 61: 5502713
  23. 23.
    Mou L C, Bruzzone L and Zhu X X. 2019. Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(2): 924-935
  24. 24.
    Nielsen A A, Conradsen K and Simpson J J. 1998. Multivariate Alteration Detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: new approaches to change detection studies. Remote Sensing of Environment, 64(1): 1-19
  25. 25.
    Qu J H, Hou S X, Dong W Q, Li Y S and Xie W Y. 2022a. A multilevel encoder-decoder attention network for change detection in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 60: 5518113
  26. 26.
    Qu J H, Xu Y S, Dong W Q, Li Y S and Du Q. 2022b. Dual-branch difference amplification graph convolutional network for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5519912
  27. 27.
    Shaw P, Uszkoreit J and Vaswani A. 2018. Self-attention with relative position representations//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). New Orleans: Association for Computational Linguistics: 464-468
  28. 28.
    Song R X, Ni W H, Cheng W and Wang X H. 2022. CSANet: cross-temporal interaction symmetric attention network for hyperspectral image change detection. IEEE Geoscience and Remote Sensing Letters, 19: 6010105
  29. 29.
    Wang Q, Yuan Z H, Du Q and Li X L. 2019. GETNET: a general end-to-end 2-D CNN framework for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing, 57(1): 3-13
  30. 30.
    Wang Y H, Hong D F, Sha J J, Gao L R, Liu L, Zhang Y G and Rong X H. 2022. Spectral-spatial-temporal transformers for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5536814
  31. 31.
    Wu C, Du B and Zhang L P. 2014. Slow feature analysis for change detection in multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 2858-2874
  32. 32.
    Yang B, Mao Y, Chen J, Liu J Q, Chen J and Yan K. 2023. Review of remote sensing change detection in deep learning: bibliometric and analysis. National Remote Sensing Bulletin, 27(9): 1988-2005
  33. 33.
    Zhang L P and Wu C. 2017. Advance and future development of change detection for multi-temporal remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(10): 1447-1459

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