предыдущий|следующий

Survey of remote sensing image registration based on deep learning

  • role： First author第一作者
  • Affiliation：

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

  • Email：lixinghua5540@whu.edu.cn
  • Introduction：李星华，研究方向为多时相遥感。E-mail： lixinghua5540@whu.edu.cn
LI Xinghua1,  
  • Affiliation：

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

AI Wenhao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

    School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China

  • Email：ruitaofeng@whu.edu.cn
  • Introduction：冯蕊涛，研究方向为遥感影像几何处理。E-mail： ruitaofeng@whu.edu.cn
FENG Ruitao12*,  
  • Affiliation：

    State Grid Hangzhou Power Supply Company, Hangzhou 310020, China

LUO Shaojie3

реферат

Remote sensing image registration is the process of spatial alignment of two or more images through geometric transformation. It is an important preprocessing operation for image fusion, change detection, agricultural monitoring and other remote sensing applications. Considering that remote sensing images have the characteristics of large-scale changes, complex ground covers and imaging modalities, although a large number of registration methods have been developed, there is still a lack of methods that can be widely used in different scenarios. Therefore, research on registration algorithms with high efficiency, high robustness, high precision and wide applicability is of great significance. In recent years, deep learning, which has achieved great success in the field of natural image and medical image registration, has provided a new method for remote sensing image registration. First, we introduced two kinds of traditional registration methods and analyzed the advantages and disadvantages of area-based and feature-based registration methods in detail from the aspects of registration accuracy, efficiency and algorithm robustness. Generally, there are two main problems in traditional methods: poor applicability and insufficient utilization of the deep semantic information of the image. Second, we focused on the important progress of deep learning in area-based registration methods and feature-based registration methods. According to the specific application purpose of deep learning, we made a more detailed division of the above two methods and summarized the advantages and disadvantages of the existing research. In addition, considering the importance of datasets for deep learning, we sorted and shared some public datasets for remote sensing image registration. Due to the great progress of earth observation technology, an increasing number of remote sensing images are being applied. Image registration is the key step of remote sensing image preprocessing and the basic research content of quantitative remote sensing analysis. In recent years, research on remote sensing image registration algorithms based on deep learning has shown an increasing trend, but it is still in the early stage, and the framework is not mature. It mainly includes but is not limited to the following shortcomings: (1) lack of open source standard datasets; (2) difficult to apply to large-scale remote sensing images; (3) insufficient utilization of geospatial information and spectral information of remote sensing images; and (4) long training time and the large computing overhead. From the perspective of data and methods, we looked forward to the application of deep learning in the field of remote sensing image registration and put forward four main research directions: (1) remote sensing image registration datasets; (2) registration methods based on hybrid models; (3) registration methods based on different neural networks; and (4) training strategies based on small samples.

ключеви́че слова́

deep learning;image registration;area-based;feature-based;registration datasets

References

  1. 1.
    Balntas V, Lenc K, Vedaldi A and Mikolajczyk K. 2017. HPatches: a benchmark and evaluation of handcrafted and learned local descriptors//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Hawaii: IEEE: 3852-3861
  2. 2.
    Barroso-Laguna A, Riba E, Ponsa D and Mikolajczyk K. 2019. Key.Net: keypoint detection by handcrafted and learned CNN filters. arXiv: 1904.00889v3
  3. 3.
    Blendowski M and Heinrich M P. 2019. Combining MRF-based deformable registration and deep binary 3D-CNN descriptors for large lung motion estimation in COPD patients. International Journal of Computer Assisted Radiology and Surgery, 14(1): 43-52
  4. 4.
    Chen H M, Varshney P K and Arora M K. 2003. Performance of mutual information similarity measure for registration of multitemporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 41(11): 2445-2454
  5. 5.
    Cheng X, Zhang L and Zheng Y F. 2018. Deep similarity learning for multimodal medical images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 6(3): 248-252
  6. 6.
    Chopra S, Hadsell R and LeCun Y. 2005. Learning a similarity metric discriminatively, with application to face verification//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego: IEEE: 539-546
  7. 7.
    de Vos B D, Berendsen F F, Viergever M A, Sokooti H, Staring M and Išgum I. 2019. A deep learning framework for unsupervised affine and deformable image registration. Medical Image Analysis, 52: 128-143
  8. 8.
    DeTone D, Malisiewicz T and Rabinovich A. 2018. SuperPoint: self-supervised interest point detection and description. arXiv: 1712.07629v4
  9. 9.
    Dong Y Y, Jiao W L, Long T F, Liu L F, He G J, Gong C J and Guo Y T. 2019. Local deep descriptor for remote sensing image feature matching. Remote Sensing, 11(4): 430
  10. 10.
    Fan R B, Hou B C, Liu J B, Yang J H and Hong Z L. 2021. Registration of multiresolution remote sensing images based on L2-Siamese model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 237-248
  11. 11.
    Feng R T, Du Q Y, Li X H and Shen H F. 2019. Robust registration for remote sensing images by combining and localizing feature- and area-based methods. ISPRS Journal of Photogrammetry and Remote Sensing, 151: 15-26
  12. 12.
    Feng R T. 2020. The Registration Algorithm for Remote Sensing Images Covering the Complex Terrain by Multi-Model Union. Wuhan: Wuhan University: 1-122
  13. 13.
    Feng R T, Shen H F, Bai J J and Li X H. 2021. Advances and opportunities in remote sensing image geometric registration: a systematic review of state-of-the-art approaches and future research directions. IEEE Geoscience and Remote Sensing Magazine, 9(4): 120-142
  14. 14.
    Feng R T, Du Q Y, Luo H, Shen H F, Li X H and Liu B. 2021. A registration algorithm based on optical flow modification for multi-temporal remote sensing images covering the complex-terrain region. National Remote Sensing Bulletin, 25(2): 630-640
  15. 15.
    Fischler M A and Bolles R C. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6): 381-395
  16. 16.
    Girard N, Charpiat G and Tarabalka Y. 2019. Aligning and updating cadaster maps with aerial images by multi-task, multi-resolution deep learning//14th Asian Conference on Computer Vision. Perth: Springer: 675-690
  17. 17.
    Gong M G, Zhao S M, Jiao L C, Tian D Y and Wang S. 2014. A novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information. IEEE Transactions on Geoscience and Remote Sensing, 52(7): 4328-4338
  18. 18.
    Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Faley D, Ozair S, Courville A and Bengio Y. 2014. Generative adversarial networks. arXiv: 1406.2661v1
  19. 19.
    Harris C and Stephens M. 1988. A combined corner and edge detector//Proceedings of the Alvey Vision Conference. Manchester: AVC: 147-151
  20. 20.
    Haskins G, Kruecker J, Kruger U, Xu S, Pinto P A, Wood B J and Yan P K. 2019. Learning deep similarity metric for 3D MR–TRUS image registration. International Journal of Computer Assisted Radiology and Surgery, 14(3): 417-425
  21. 21.
    He H Q, Chen M, Chen T and Li D J. 2018. Matching of remote sensing images with complex background variations via Siamese convolutional neural network. Remote Sensing, 10(2): 355
  22. 22.
    He H Q, Chen M, Chen T, Li D J and Cheng P G. 2019. Learning to match multitemporal optical satellite images using multi-support-patches Siamese networks. Remote Sensing Letters, 10(6): 516-525
  23. 23.
    Hinton G E and Salakhutdinov R R. 2006. Reducing the dimensionality of data with neural networks. Science, 313(5786): 504-507
  24. 24.
    Holland P W and Welsch R E. 1977. Robust regression using iteratively reweighted least-squares. Communications in Statistics-Theory and Methods, 6(9): 813-827
  25. 25.
    Hughes L H, Schmitt M, Mou L C, Wang Y Y and Zhu X X. 2018. Identifying corresponding patches in SAR and optical images with a Pseudo-Siamese CNN. IEEE Geoscience and Remote Sensing Letters, 15(5): 784-788
  26. 26.
    Hui T W, Tang X O and Loy C C. 2018. LiteFlowNet: a lightweight convolutional neural network for optical flow estimation//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE: 8981-8989
  27. 27.
    Hui T W and Loy C C. 2020. LiteFlowNet3: resolving correspondence ambiguity for more accurate optical flow estimation//16th European Conference on Computer Vision. Glasgow: Springer: 169-184
  28. 28.
    Hui T W, Tang X O and Loy C C. 2021. A lightweight optical flow CNN—revisiting data fidelity and regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(8): 2555-2569
  29. 29.
    Jiang X Y, Ma J Y, Xiao G B, Shao Z F and Guo X J. 2021a. A review of multimodal image matching: methods and applications. Information Fusion, 73: 22-71
  30. 30.
    Jiang X Y, Ma J Y, Fan A X, Xu H P, Lin G, Lu T and Tian X. 2021b. Robust feature matching for remote sensing image registration via linear adaptive filtering. IEEE Transactions on Geoscience and Remote Sensing, 59(2): 1577-1591
  31. 31.
    Johnson K, Cole-Rhodes A, Zavorin I and Le Moigne J. 2001. Mutual information as a similarity measure for remote sensing image registration//Proceedings Volume 4383, Geo-Spatial Image and Data Exploitation II. Orlando: SPIE: 51-61
  32. 32.
    Kim D G, Nam W J and Lee S W. 2019. A robust matching network for gradually estimating geometric transformation on remote sensing imagery//2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). Bari: IEEE: 3889-3894
  33. 33.
    Kuppala K, Banda S and Barige T R. 2020. An overview of deep learning methods for image registration with focus on feature-based approaches. International Journal of Image and Data Fusion, 11(2): 113-135
  34. 34.
    Lan C Z, Lu W J, Yu J M and Xu Q. 2021. Deep learning algorithm for feature matching of cross modality remote sensing images. Acta Geodaetica et Cartographica Sinica, 50(2): 189-202
  35. 35.
    LeCun Y, Bottou L, Bengio Y and Haffner P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11): 2278-2324
  36. 36.
    LeCun Y, Bengio Y and Hinton G. 2015. Deep learning. Nature, 521(7553): 436-444
  37. 37.
    Lee W, Sim D and Oh S J. 2021. A CNN-based high-accuracy registration for remote sensing images. Remote Sensing, 13(8): 1482
  38. 38.
    Lee W J and Oh S J. 2021. Remote sensing image registration using equivariance features//2021 International Conference on Information Networking (ICOIN). Bangkok: IEEE: 776-781
  39. 39.
    Li J Y, Hu Q W and Ai M Y. 2020a. RIFT: multi-modal image matching based on radiation-variation insensitive feature transform. IEEE Transactions on Image Processing, 29: 3296-3310
  40. 40.
    Li J Y, Zhao P C, Hu Q W and Ai M Y. 2020b. Robust point cloud registration based on topological graph and Cauchy weighted lq-norm. ISPRS Journal of Photogrammetry and Remote Sensing, 160: 244-259
  41. 41.
    Li L, Ji S, Yu Y and Zhang Y S. 2020. A multi-feature-based registration method adapted to multi-source remote sensing images. Journal of Geomatics Science and Technology, 37(1): 74-78
  42. 42.
    Li X H, Feng R T, Guan X B, Shen H F and Zhang L P. 2019. Remote sensing image mosaicking: achievements and challenges. IEEE Geoscience and Remote Sensing Magazine, 7(4): 8-22
  43. 43.
    Li X H, Du Z S, Huang Y Y and Tan Z Y. 2021. A deep translation (GAN) based change detection network for optical and SAR remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 179: 14-34
  44. 44.
    Liang J Y, Liu X P, Huang K N, Li X, Wang D G and Wang X W. 2014. Automatic registration of multisensor images using an integrated spatial and mutual information (SMI) metric. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 603-615
  45. 45.
    Liang Y, Sheng Y H, Zhang K and Yang L. 2014. Linear feature matching method based on local affine invariant and Epipolar constraint for close-range images. Geomatics and Information Science of Wuhan University, 39(2): 229-233
  46. 46.
    Liao R, Miao S, de Tournemire P, Grbic S, Kamen A, Mansi T and Comaniciu D. 2016. An artificial agent for robust image registration. arXiv: 1611.10336v1
  47. 47.
    Liu P P, Irwin K, Lyu M R and Xu J. 2019a. DDFlow: learning optical flow with unlabeled data distillation. Proceedings of the AAAI Conference on Artificial Intelligence, 33(1): 8770-8777
  48. 48.
    Liu P P, Lyu M, King I and Xu J. 2019b. SelFlow: self-supervised learning of optical flow//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE: 4571-4580
  49. 49.
    Lowe D G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2): 91-110
  50. 50.
    Lu J Y, Jia H G, Li T, Li Z Q, Ma J Y and Zhu R F. 2021. An instance segmentation based framework for large-sized high-resolution remote sensing images registration. Remote Sensing, 13(9): 1657
  51. 51.
    Luo Y, Cao W M, He Z Q, Zou W L and He Z H. 2021. Deformable adversarial registration network with multiple loss constraints. Computerized Medical Imaging and Graphics, 91: 101931
  52. 52.
    Ma D A, Tang P, Zhao L J and Zhang Z. 2021. Review of data augmentation for image in deep learning. Journal of Image and Graphics, 26(3): 487-502
  53. 53.
    Ma F L. 2018. Research on Color-To-Gray Conversion based on Normalized Cross Correlation. Lanzhou: Lanzhou University: 1-61
  54. 54.
    Ma J Y, Jiang X Y, Jiang J J, Zhao J and Guo X J. 2019c. LMR: learning a two-class classifier for mismatch removal. IEEE Transactions on Image Processing, 28(8): 4045-4059
  55. 55.
    Ma J Y, Jiang X Y, Fan A X, Jiang J J and Yan J C. 2021. Image matching from handcrafted to deep features: a survey. International Journal of Computer Vision, 129(1): 23-79
  56. 56.
    Ma L, Liu Y, Zhang X L, Ye Y X, Yin G F and Johnson B A. 2019a. Deep learning in remote sensing applications: a meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152: 166-177
  57. 57.
    Ma W P, Zhang J, Wu Y, Jiao L C, Zhu H and Zhao W. 2019b. A novel two-step registration method for remote sensing images based on deep and local features. IEEE Transactions on Geoscience and Remote Sensing, 57(7): 4834-4843
  58. 58.
    Ni H, Feng Z, Guan Y, Jia X Y, Chen W, Jiang T, Zhong Q Y, Yuan J, Ren M, Li X N, Gong H, Luo Q M and Li A N. 2021. DeepMapi: a fully automatic registration method for mesoscopic optical brain images using convolutional neural networks. Neuroinformatics, 19(2): 267-284
  59. 59.
    Niu R G, Sun X, Tian Y, Diao W H, Chen K Q and Fu K. 2020. Hybrid multiple attention network for semantic segmentation in aerial images. arXiv: 2001.02870v3
  60. 60.
    Park J H, Nam W J and Lee S W. 2020. A two-stream symmetric network with bidirectional ensemble for aerial image matching. Remote Sensing, 12(3): 465
  61. 61.
    Rahaghi A I, Lemmin U, Sage D and Barry D A. 2019. Achieving high-resolution thermal imagery in low-contrast lake surface waters by aerial remote sensing and image registration. Remote Sensing of Environment, 221: 773-783
  62. 62.
    Ranjan A and Black M J. 2017. Optical flow estimation using a spatial pyramid network//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Hawaii: IEEE: 2720-2729
  63. 63.
    Rocco I, Arandjelovic R and Sivic J. 2017. Convolutional neural network architecture for geometric matching//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Hawaii: IEEE: 39-48
  64. 64.
    Savinov N, Seki A, Ladicky L, Sattler T and Pollefeys M. 2017. Quad-networks: unsupervised learning to rank for interest point detection. arXiv: 1611.07571v2
  65. 65.
    Schmidhuber J. 2015. Deep learning in neural networks: an overview. Neural Networks, 61: 85-117
  66. 66.
    Schmitt M, Hughes L H and Zhu X X. 2018. The SEN1-2 dataset for deep learning in SAR-optical data fusion. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-1: 141-146
  67. 67.
    Seo S, Choi J S, Lee J, Kim H H, Seo D, Jeong J and Kim M. 2020. UPSNet: unsupervised pan-sharpening network with registration learning between panchromatic and multi-spectral images. IEEE Access, 8: 201199-201217
  68. 68.
    Shabanov A, Gladilin S and Shvets E. 2020. Optical-to-SAR image registration using a combination of CNN descriptors and cross-correlation coefficient//Twelfth International Conference on Machine Vision (ICMV). Amsterdam: SPIE: 440-449
  69. 69.
    Shelhamer E, Long J and Darrell T. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4): 640-651
  70. 70.
    Simonovsky M, Gutiérrez-Becker B, Mateus D, Navab N and Komodakis N. 2016. A deep metric for multimodal registration//19th International Conference on Medical Image Computing and Computer-Assisted Intervention. Athens: Springer: 10-18
  71. 71.
    Simonyan K and Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition. arXiv: 1409.1556v6
  72. 72.
    Smith S M and Brady J M. 1997. SUSAN-a new approach to low level image processing. International Journal of Computer Vision, 23(1): 45-78
  73. 73.
    Suri S and Reinartz P. 2010. Mutual-information-based registration of TerraSAR-X and Ikonos imagery in urban areas. IEEE Transactions on Geoscience and Remote Sensing, 48(2): 939-949
  74. 74.
    Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L and Polosukhin I. 2017. Attention is all you need. arXiv: 1706.03762v5
  75. 75.
    Verdie Y, Yi K M, Fua P and Lepetit V. 2015. TILDE: a temporally invariant learned detector//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston: IEEE: 5279-5288
  76. 76.
    Wang S, Quan D, Liang X F, Ning M D, Guo Y H and Jiao L C. 2018. A deep learning framework for remote sensing image registration. ISPRS Journal of Photogrammetry and Remote Sensing, 145: 148-164
  77. 77.
    Wang Y Y and Zhu X X. 2018. The SARptical dataset for joint analysis of SAR and optical image in dense urban area. arXiv: 1801.07532
  78. 78.
    Wang Z H and Wu F C. 2009. Mean-standard deviation descriptor and line matching. Pattern Recognition and Artificial Intelligence, 22(1): 32-39
  79. 79.
    Xiang Y M, Tao R S, Wang F, You H J and Han B. 2020. Automatic registration of optical and SAR images via improved phase congruency model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 5847-5861
  80. 80.
    Yang J, Yang J H, Zhao F and Zhang W J. 2021. An unsupervised multi-scale framework with attention-based network (MANet) for lung 4D-CT registration. Physics in Medicine and Biology, 66(13): 135008
  81. 81.
    Yang Z Q, Dan T T and Yang Y. 2018. Multi-temporal remote sensing image registration using deep convolutional features. IEEE Access, 6: 38544-38555
  82. 82.
    Yao M Q and Hu J. 2020. Robust multimodal medical image registration using deep recurrent reinforcement learning. Journal of Computer-Aided Design and Computer Graphics, 32(8): 1236-1247
  83. 83.
    Ye F M, Su Y F, Xiao H, Zhao X Q and Min W D. 2018. Remote sensing image registration using convolutional neural network features. IEEE Geoscience and Remote Sensing Letters, 15(2): 232-236
  84. 84.
    Ye Y X and Shan J. 2014. A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences. ISPRS Journal of Photogrammetry and Remote Sensing, 90: 83-95
  85. 85.
    Yi K M, Trulls E, Lepetit V and Fua P. 2016. LIFT: learned invariant feature transform//14th European Conference on Computer Vision (ECCV). Amsterdam: Springer: 467-483
  86. 86.
    Yi K M, Trulls E, Ono Y, Lepetit V, Salzmann M and Fua P. 2018. Learning to find good correspondences//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE: 2666-2674
  87. 87.
    Yu X C, Lü Z H and Hu D. 2013. Review of remote sensing image registration techniques. Optics and Precision Engineering, 21(11): 2960-2972
  88. 88.
    Yuan Z, Guo H T, Lu J, Lu W and Lin Y Z. 2021. High-resolution remote sensing image change detection technology based on UNet++ and attention mechanism. Journal of Geomatics Science and Technology, 38(2): 155-159
  89. 89.
    Zampieri A, Charpiat G, Girard N and Tarabalka Y. 2018. Multimodal image alignment through a multiscale chain of neural networks with application to remote sensing//15th European Conference on Computer Vision (ECCV). Munich: Springer: 679-696
  90. 90.
    Zeng L, Du Y L, Lin H P, Wang J, Yin J J and Yang J. 2021. A novel region-based image registration method for multisource remote sensing images via CNN. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 1821-1831
  91. 91.
    Zeng Y, Ning Z H, Liu P, Luo P L, Zhang Y and He G J. 2020. A mosaic method for multi-temporal data registration by using convolutional neural networks for forestry remote sensing applications. Computing, 102(3): 795-811
  92. 92.
    Zhang H, Ni W P, Yan W D, Xiang D L, Wu J Z, Yang X L and Bian H. 2019a. Registration of multimodal remote sensing image based on deep fully convolutional neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(8): 3028-3042
  93. 93.
    Zhang J, Ma W P, Wu Y and Jiao L C. 2019b. Multimodal remote sensing image registration based on image transfer and local features. IEEE Geoscience and Remote Sensing Letters, 16(8): 1210-1214
  94. 94.
    Zhang L G and Rusinkiewicz S. 2018. Learning to detect features in texture images//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE: 6325-6333
  95. 95.
    Zhao L Y, Lü B Y, Li X R and Chen S H. 2015. Multi-source remote sensing image registration based on scale-invariant feature transform and optimization of regional mutual information. Acta Physica Sinica, 64(12): 124204
  96. 96.
    Zheng Z D, Wei Y C and Yang Y. 2020. University-1652: a multi-view multi-source benchmark for drone-based geo-localization. arXiv: 2002.12186v2
  97. 97.
    Zhou Z H. 2018. A brief introduction to weakly supervised learning. National Science Review, 5(1): 44-53
  98. 98.
    Zhu H, Jiao L C, Ma W P, Liu F and Zhao W. 2019a. A novel neural network for remote sensing image matching. IEEE Transactions on Neural Networks and Learning Systems, 30(9): 2853-2865
  99. 99.
    Zhu R J, Yu D W, Ji S P and Lu M. 2019b. Matching RGB and infrared remote sensing images with densely-connected convolutional neural networks. Remote Sensing, 11(23): 2836

Читать полностью

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