Früher|Nächster

Remote sensing change detection technology in the Era of artificial intelligence： Inheritance， development and challenges

  • role： First author第一作者
  • Affiliation：

    College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China

  • Email：sicong.liu@tongji.edu.cn
  • Introduction：柳思聪，研究方向为遥感光谱探测与多时相变化检测。E-mail： sicong.liu@tongji.edu.cn
LIU Sicong1,  
  • Affiliation：

    College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China

DU Kecheng1,  
  • Affiliation：

    College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China

ZHENG Yongjie1,  
  • Affiliation：

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China

CHEN Jin2,  
  • Affiliation：

    School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

DU Peijun3,  
  • Affiliation：

    College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China

TONG Xiaohua1

Resümee

In the past decades, the effects of global climate change and the increase of human activities have remarkably increased the demand for remote sensing monitoring. Moreover, with the accumulation of remote sensing data from multiple platforms and multiple sensors, the quantity and quality of multitemporal images have substantially improved. Multitemporal remote sensing images Change Detection (CD) is a processing and analysis technology that aims to automatically detect, identify, and describe changes occurring in the same geographical area at different times. With the advancement of remote sensing and Artificial Intelligence (AI) technology, traditional data-driven and modal CD methods are evolving toward data-model-knowledge jointly driven direction to solve the land surface spatio-temporal CD problem in a variety of application fields in a more automatic, refined, and intelligent manner. This paper first summarizes existing problems in multitemporal remote sensing CD by analyzing the use of homogeneous and heterogenous data sources, developments from traditional to intelligent CD models, and challenges from theoretical to practical CD applications. Optical image CD is taken as an example, and the evolution of CD technology in the era of AI is examined, which can be summarized as three periods of data-driven CD, model-driven CD, and data-model-knowledge driven CD. Then, the characteristics and problems of each periods are discussed. Furthermore, for each of the three aspects (unsupervised, supervised, and weakly supervised), the characteristics and trends in the development of traditional to cutting-edge CD techniques are discussed. In the future, one can focus on breaking through key issues such as the physical interpretability, generalization, and transferability of the CD models as well as their successful implementation in cross-data, cross-scene, and cross-domain applications.

Schlüsselwort

remote sensing;change detection;multi-temporal analysis;artificial intelligence;machine learning;deep learning

References

  1. 1.
    Bovolo F, Bruzzone L and Marconcini M. 2008. A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure. IEEE Transactions on Geoscience and Remote Sensing, 46(7): 2070-2082
  2. 2.
    Bovolo F, Camps-Valls G and Bruzzone L. 2010. A support vector domain method for change detection in multitemporal images. Pattern Recognition Letters, 31(10): 1148-1154
  3. 3.
    Bovolo F, Marchesi S and Bruzzone L. 2012. A framework for automatic and unsupervised detection of multiple changes in multitemporal images. IEEE Transactions on Geoscience and Remote Sensing, 50(6): 2196-2212
  4. 4.
    Bruzzone L and Prieto D F. 2000. Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1171-1182
  5. 5.
    Chen J, Chen X H, Cui X H and Chen J. 2011. Change vector analysis in posterior probability space: a new method for land cover change detection. IEEE Geoscience and Remote Sensing Letters, 8(2): 317-321
  6. 6.
    Chen J, Gong P, He C Y, Pu R L and Shi P J. 2003. Land-use/land-cover change detection using improved change-vector analysis. Photogrammetric Engineering and Remote Sensing, 69(4): 369-379
  7. 7.
    Chen X H, Chen J, Shi Y S and Yamaguchi Y. 2012. An automated approach for updating land cover maps based on integrated change detection and classification methods. ISPRS Journal of Photogrammetry and Remote Sensing, 71: 86-95
  8. 8.
    Coppin P, Jonckheere I, Nackaerts K, Muys B and Lambin E. 2004. Review Article Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 25(9): 1565-1596
  9. 9.
    Dalponte M, Jucker T, Liu S C, Frizzera L and Gianelle D. 2019. Characterizing forest carbon dynamics using multi-temporal lidar data. Remote Sensing of Environment, 224: 412-420
  10. 10.
    Demir B, Bovolo F and Bruzzone L. 2013. Updating land-cover maps by classification of image time series: a novel change-detection-driven transfer learning approach. IEEE Transactions on Geoscience and Remote Sensing, 51(1): 300-312
  11. 11.
    Dian Y Y. 2005. Research on Change Detection based in Remote Sensing Imagery. Wuhan: Wuhan University
  12. 12.
    Ding L, Guo H T, Liu S C, Mou L C, Zhang J and Bruzzone L. 2022. Bi-temporal semantic reasoning for the semantic change detection in hr remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60: 5620014
  13. 13.
    Du B, Ru L X, Wu C and Zhang L P. 2019. Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57(12): 9976-9992
  14. 14.
    Du P J and Liu S C. 2012c. Change detection from multi-temporal remote sensing images by integrating multiple features. Journal of Remote Sensing, 16(4): 663-677
  15. 15.
    Du P J, Liu S C, Gamba P, Tan K and Xia J S. 2012. Fusion of difference images for change detection over urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(4): 1076-1086
  16. 16.
    Du P J, Liu S C, Liu P, Tan K and Cheng L. 2014. Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images. Geo-spatial Information Science, 17(1): 26-38
  17. 17.
    Du P J, Liu S C and Tan K. 2012b. Rapid monitoring of japan earthquake-triggered tsunami disaster based on a fusion of multiple features derived from HJ small satellite images. Science and Technology Review, 30(4): 31-36
  18. 18.
    Du P J, Liu S C, Xia J S and Zhao Y D. 2013. Information fusion techniques for change detection from multi-temporal remote sensing images. Information Fusion, 14(1): 19-27
  19. 19.
    Du P J, Liu S C and Zheng H. 2012a. Land cover change detection over mining areas based on support vector machine. Journal of China University of Mining and Technology, 41(2): 262-267
  20. 20.
    Feng W Q, Sui H G, Tu J H, Huang W M, Xu C and Sun K M. 2018. A novel change detection approach for multi-temporal high-resolution remote sensing images based on rotation forest and coarse-to-fine uncertainty analyses. Remote Sensing, 10(7): 1015
  21. 21.
    Guo Q L, Zhang J P, Zhong C X and Zhang Y. 2021. Change detection for hyperspectral images via convolutional sparse analysis and temporal spectral unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 4417-4426
  22. 22.
    Han Y and Byun Y. 2015. Automatic and accurate registration of VHR optical and SAR images using a quadtree structure. International Journal of Remote Sensing, 36(9): 2277-2295
  23. 23.
    Jiang F L, Gong M G, Zhan T and Fan X L. 2020. A semisupervised GAN-based multiple change detection framework in multi-spectral images. IEEE Geoscience and Remote Sensing Letters, 17(7): 1223-1227
  24. 24.
    Jiang X, Li G, Zhang X P and He Y. 2022. A semisupervised Siamese network for efficient change detection in heterogeneous remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60: 4700718
  25. 25.
    Kasetkasem T and Varshney P K. 2002. An image change detection algorithm based on Markov random field models. IEEE Transactions on Geoscience and Remote Sensing, 40(8): 1815-1823
  26. 26.
    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
  27. 27.
    Li S T, Li C Y and Kang X D. 2021. Development status and future prospects of multi-source remote sensing image fusion. National Remote Sensing Bulletin, 25(1): 148-166
  28. 28.
    Liu S C, Bovolo F, Bruzzone L, Du Q and Tong X H. 2021. Unsupervised change detection in multitemporal remote sensing images//Change Detection and Image Time Series Analysis 1: Unsupervised Methods. [s.l.]: John Wiley & Sons: 1-34
  29. 29.
    Liu S C, Bruzzone L, Bovolo F and Du P J. 2012. Unsupervised hierarchical spectral analysis for change detection in hyperspectral images//2012 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Shanghai: IEEE: 1-4
  30. 30.
    Liu S C, Bruzzone L, Bovolo F and Du P J. 2015b. Hierarchical unsupervised change detection in multitemporal hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 53(1): 244-260
  31. 31.
    Liu S C, Bruzzone L, Bovolo F and Du P J. 2016. Unsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 54(5): 2733-2748
  32. 32.
    Liu S C, Bruzzone L, Bovolo F, Zanetti M and Du P J. 2015a. Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 53(8): 4363-4378
  33. 33.
    Liu S C, Chi M M, Zou Y X, Samat A, Benediktsson J A and Plaza A. 2017a. Oil spill detection via multitemporal optical remote sensing images: a change detection perspective. IEEE Geoscience and Remote Sensing Letters, 14(3): 324-328
  34. 34.
    Liu S C, Du Q, Tong X H, Samat A and Bruzzone L. 2019a. Unsupervised change detection in multispectral remote sensing images via spectral-spatial band expansion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9): 3578-3587
  35. 35.
    Liu S C, Du Q, Tong X H, Samat A, Bruzzone L and Bovolo F. 2017b. Multiscale morphological compressed change vector analysis for unsupervised multiple change detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9): 4124-4137
  36. 36.
    Liu S C, Du Q, Tong X H, Samat A, Pan H Y and Ma X L. 2017c. Band selection-based dimensionality reduction for change detection in multi-temporal hyperspectral images. Remote Sensing, 9(10): 1008
  37. 37.
    Liu S C, Marinelli D, Bruzzone L and Bovolo F. 2019b. A review of change detection in multitemporal hyperspectral images: current techniques, applications, and challenges. IEEE Geoscience and Remote Sensing Magazine, 7(2): 140-158
  38. 38.
    Liu S C, Tong X H, Bruzzone L and Du P J. 2017d. A novel semisupervised framework for multiple change detection in hyperspectral images//2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Fort Worth: IEEE: 173-176
  39. 39.
    Lu D, Mausel P, Brondízio E and Moran E. 2004. Change detection techniques. International Journal of Remote Sensing, 25(12): 2365-2401
  40. 40.
    Luppino L T, Bianchi F M, Moser G and Anfinsen S N. 2019. Unsupervised image regression for heterogeneous change detection. IEEE Transactions on Geoscience and Remote Sensing, 57(12): 9960-9975
  41. 41.
    Lv Z Y, Liu T F, Shi C, Benediktsson J A and Du H J. 2019. Novel land cover change detection method based on k-means clustering and adaptive majority voting using bitemporal remote sensing images. IEEE Access, 7: 34425-34437
  42. 42.
    Nemmour H and Chibani Y. 2006. Multiple support vector machines for land cover change detection: an application for mapping urban extensions. ISPRS Journal of Photogrammetry and Remote Sensing, 61(2): 125-133
  43. 43.
    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
  44. 44.
    Nielsen A A. 2007. The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data. IEEE Transactions on Image Processing, 16(2): 463-478
  45. 45.
    Pekel J F, Cottam A, Gorelick N and Belward A S. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418-422
  46. 46.
    Peng D F, Bruzzone L, Zhang Y J, Guan H Y, Ding H Y and Huang X. 2021. SemiCDNet: a semisupervised convolutional neural network for change detection in high resolution remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 59(7): 5891-5906
  47. 47.
    Peng D F, Zhang Y J and Guan H Y. 2019. End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sensing, 11(11): 1382
  48. 48.
    Roy M, Ghosh S and Ghosh A. 2014. A novel approach for change detection of remotely sensed images using semi-supervised multiple classifier system. Information Sciences, 269: 35-47
  49. 49.
    Saha S, Bovolo F and Bruzzone L. 2019. Unsupervised deep change vector analysis for multiple-change detection in VHR images. IEEE Transactions on Geoscience and Remote Sensing, 57(6): 3677-3693
  50. 50.
    Shafique A, Cao G, Khan Z, Asad M and Aslam M. 2022. Deep learning-based change detection in remote sensing images: a review. Remote Sensing, 14(4): 871
  51. 51.
    Shi Q, Liu M X, Li S C, Liu X P, Wang F and Zhang L P. 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: 5604816
  52. 52.
    Shi W Z, Zhang M, Zhang R, Chen S X and Zhan Z. 2020. Change detection based on artificial intelligence: state-of-the-art and challenges. Remote Sensing, 12(10): 1688
  53. 53.
    Singh A. 1989. Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6): 989-1003
  54. 54.
    Soares V P and Hoffer R M. 1995. Eucalyptus forest change classification using multi-date Landsat TM data//Proceedings Volume 2314, Multispectral and Microwave Sensing of Forestry, Hydrology, and Natural Resources. Rome: SPIE
  55. 55.
    Song X P, Hansen M C, Stehman S V, Potapov P V, Tyukavina A, Vermote E F and Townshend J R. 2018. Global land change from 1982 to 2016. Nature, 560(7720): 639-643
  56. 56.
    Sui H G, Feng W Q, Li W Z, Sun K M and Xu C. 2018. Review of change detection methods for multi-temporal remote sensing imagery. Geomatics and Information Science of Wuhan University, 43(12): 1885-1898
  57. 57.
    Tan K, Zhang Y S, Wang X and Chen Y. 2019. Object-based change detection using multiple classifiers and multi-scale uncertainty analysis. Remote Sensing, 11(3): 359
  58. 58.
    Tang X, Zhang H Y, Mou L C, Liu F, Zhang X R, Zhu X X and Jiao L C. 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: 5609715
  59. 59.
    Tong G F, Li Y, Ding W L and Yue X Y. 2015. Review of remote sensing image change detection. Journal of Image and Graphics, 20(12): 1561-1571
  60. 60.
    Tong X H, Pan H Y, Liu S C, Li B B, Luo X, Xie H and Xu X. 2020. A novel approach for hyperspectral change detection based on uncertain area analysis and improved transfer learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 2056-2069
  61. 61.
    Volpi M, Tuia D, Bovolo F, Kanevski M and Bruzzone L. 2013. Supervised change detection in VHR images using contextual information and support vector machines. International Journal of Applied Earth Observation and Geoinformation, 20: 77-85
  62. 62.
    Wang L F, Wang L G, Wang Q M and Atkinson P M. 2022. SSA-SiamNet: spectral-spatial-wise attention-based Siamese network for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5510018
  63. 63.
    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
  64. 64.
    Wang X, Du P J, Chen D M, Liu S C, Zhang W and Li E Z. 2020. Change detection based on low-level to high-level features integration with limited samples. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 6260-6276
  65. 65.
    Wang X, Liu S C, Du P J, Liang H, Xia J S and Li Y F. 2018. Object-based change detection in urban areas from high spatial resolution images based on multiple features and ensemble learning. Remote Sensing, 10(2): 276
  66. 66.
    Wen D W, Huang X, Bovolo F, Li J Y, Ke X L, Zhang A L and Benediktsson J A. 2021. Change detection from very-high-spatial-resolution optical remote sensing images: methods, applications, and future directions. IEEE Geoscience and Remote Sensing Magazine, 9(4): 68-101
  67. 67.
    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
  68. 68.
    Wu C, Zhang L P and Du B. 2017. Kernel slow feature analysis for scene change detection. IEEE Transactions on Geoscience and Remote Sensing, 55(4): 2367-2384
  69. 69.
    Yang M J, Jiao L C, Liu F, Hou B and Yang S Y. 2019. Transferred deep learning-based change detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57 (9): 6960-6973
  70. 70.
    Zanetti M, Bovolo F and Bruzzone L. 2015. Rayleigh-rice mixture parameter estimation via EM algorithm for change detection in multispectral images. IEEE Transactions on Image Processing, 24(12): 5004-5016
  71. 71.
    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

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website