предыдущий|следующий

Deep-learning approaches for pixel-level pansharpening

  • role： First author第一作者
  • Affiliation：

    School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China

  • Email：greatyangy@126.com
  • Introduction：杨勇， 研究方向为遥感图像处理、计算机视觉与机器学习。E-mail: greatyangy@126.com
YANG Yong1,  
  • Affiliation：

    School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China

SU Zhao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Software, Tiangong University, Tianjin 300387, China

  • Email：shuyinghuang2010@126.com
  • Introduction：黄淑英，研究方向为图像处理、人工智能。E-mail: shuyinghuang2010@126.com
HUANG Shuying2*,  
  • Affiliation：

    School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330032, China

WAN Weiguo3,  
  • Affiliation：

    School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China

TU Wei1,  
  • Affiliation：

    School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China

LU Hangyuan1

реферат

Pansharpening is a fundamental problem in the field of remote sensing data processing. It has important research significance and application value in ground object classification and ground surface change detection. In recent years, Deep Learning (DL) has made great progress in natural language processing, computer vision, etc. and has promoted the development of pixel-level pansharpening technology. This work presents a systematic review of the research of DL in pansharpening from two aspects (classical and collaborative approaches) and makes a prospect on this basis. First, the common datasets of pansharpening and the objective evaluation indexes of pansharpening, including reference and non-reference quality evaluation indexes, are provided. Second, the latest research results of DL-based pansharpening are introduced in two different categories from the classical and collaborative methods, and the performance of their algorithms is compared, analyzed, and summarized. The classical methods mainly include AE-based pansharpening, CNN-based pansharpening, DRN-based pansharpening, and GAN-based pansharpening methods. Meanwhile, the collaborative methods mainly include DL+CS-based pansharpening, DL+MRA-based pansharpening, DL+MB-based pansharpening, DL+injection model-based pansharpening, CNN+DRN-based pansharpening, and RNN+CNN-based pansharpening methods. In the comparative analysis of the classical and collaborative methods, the common point is that the DL technology can automatically learn the advantages of complex data features and extract the feature information of the MS or PAN image (i.e., the information that needs to be retained in the HRMS fusion image). The difference is that the structure of the classical mode is more concise, while that of the collaborative mode is more complex because it is the combination of multiple methods or frameworks. In addition, most early DL-based pansharpening methods utilized the powerful data fitting ability of the DL model and seldom paid attention to the field of pansharpening problems. With the gradual deepening of research, such as using DL methods combined with traditional pansharpening methods, this designed fusion model considers spectral and spatial distortions. Accordingly, the DL methods can further enhance the pansharpening effect. Thirdly, the three main application fields of pansharpening are analyzed, such as object classification, target recognition, and surface change detection. Finally, this work discusses the future research direction of DL-based pansharpening in combination with remote sensing knowledge to fully tap the potential of DL to obtain fused images with richer details and more natural spectra. For example, for the evaluation of pansharpening application, the performance of pansharpening in a certain application is related not only to the high quality of fusion image but also to the knowledge of a specific application field. Accordingly, the application-oriented pansharpening evaluation algorithms will be the focus of future study. Furthermore, DL-based pansharpening needs to train a large number of network parameters, resulting in a longer training time for the pansharpening model. The lightweight depth model has a smaller network capacity, lower time complexity, and lower hardware requirements. Therefore, constructing a lightweight pansharpening model is a promising future direction.

ключеви́че слова́

pansharpening;deep learning;classical mode;collaborative mode;quality evaluation;remote sensing image fusion

References

  1. 1.
    Aguilar M A, Saldaña M M and Aguilar F. 2013. GeoEye-1 and worldview-2 pan-sharpened imagery for object-based classification in urban environments. International Journal of Remote Sensing, 34(7):2583-2606
  2. 2.
    Aiazzi B, Baronti S and Selva M. 2007. Improving component substitution pansharpening through multivariate regression of ms+pan data. IEEE Transactions on Geoscience and Remote Sensing, 45(10):3230-3239
  3. 3.
    Alparone L, Aiazzi B, Baronti S and Garzelli A. 2015. Remote sensing image fusion. London, UK: CRC Press
  4. 4.
    Alparone L, Wald L, Chanussot J, Thomas C, Gamba P and Bruce L M. 2007. Comparison of pansharpening algorithms: outcome of the 2006 grs-s data-fusion contest. IEEE Transactions on Geoscience and Remote Sensing, 45(10):3012-3021
  5. 5.
    Azarang A and Ghassemian H. 2017. A new pansharpening method using multi resolution analysis framework and deep neural networks. // Proceedings of the International Conference on Pattern Recognition and Image Analysis. Shahrekord, Iran, 1-6
  6. 6.
    Azarang A, Manoochehri H E and Kehtarnavaz N. 2019. Convolutional autoencoder-based multispectral image fusion. IEEE Access, 7:35673-35683
  7. 7.
    Benzenati T, Kessentini Y, Kallel A and Hallabia H. 2020. Generalized laplacian pyramid pan-sharpening gain injection prediction based on cnn. IEEE Geoscience and Remote Sensing Letters, 17(4):651-655
  8. 8.
    Bovolo F, Bruzzone L, Capobianco L, Garzelli A, Marchesi S and Nencini F. 2010. Analysis of the effects of pansharpening in change detection on VHR images. IEEE Geoscience and Remote Sensing Letters, 7(1): 53-57
  9. 9.
    Bowman S R, Angeli G, Potts C and Manning C D. 2015. A large annotated corpus for learning　natural language　inference//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Lisbon, Portugal: 632-642
  10. 10.
    Cai W T, Xu Y, Wu Z B, Liu H Y, Qian L and Wei Z H. 2018. Pan-sharpening based on multilevel coupled deep network. // Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain: 7046-7049
  11. 11.
    Carper W J, Lillesand T M and Kiefer P W. 1990. The use of intensity-hue-saturation transformations for merging spot panchromatic and multispectral image data. Photogrammetric Engineering and Remote Sensing, 56(4):459–467
  12. 12.
    Chen C, Fu J Q, Gai Y Y, Li J, Chen L, Mantravadi V S and Tan A H. 2018. Damaged bridges over water: using high-spatial-resolution remote-sensing images for recognition, detection, and assessment. IEEE Geoscience and Remote Sensing Magazine, 6(3):69-85
  13. 13.
    Chen C, Fu J Q, Lu N, Chu YL, Hu J C, Guo B Y and Zhao X. 2019. Knowledge-based identification and damage detection of bridges spanning water via high-spatial-resolution optical remotely sensed imagery. Journal of the Indian Society of Remote Sensing, 47(12):1999-2008
  14. 14.
    Chen F J, Zhu F, Wu Q X, Hao Y M, Wang E D and Cui Y G. 2021. A survey about image generation with generative adversarial nets. Chinese Journal of Computers, 44(2): 347-369
  15. 15.
    Chen J F, Pan Y and Chen Y. 2020. Remote sensing image fusion based on Bayesian GAN. ArXiv Preprint ArXiv: 2009.09465
  16. 16.
    Chen M M, Guo Q, Liu M L and Li A. 2021. Pan-sharpening by residual network with dense convolution for remote sensing images. Journal of Remote Sensing，25（6）：1270-1283
  17. 17.
    Da Cunha A L, Zhou J P and Do M N. 2006. The nonsubsampled contourlet transform: theory, design, and applications. IEEE Transactions on Image Processing, 15(10):3089-3101
  18. 18.
    Deng L J, Vivone G, Jin C and Chanussot J. 2020. Detail injection-based deep convolutional neural networks for pansharpening. IEEE Transactions on Geoscience and Remote Sensing, 59(8):6995-7010
  19. 19.
    Dong C, Loy C C, He K M and Tang X O. 2016. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2):295-307
  20. 20.
    Eghbalian S and Ghassemian H. 2018. Multi spectral image fusion by deep convolutional neural network and new spectral loss function. International Journal of Remote Sensing, 39(12):3983-4002
  21. 21.
    Garzelli A, Capobianco L and Nencini F. 2009. On the effects of pan-sharpening to target detection//Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Cape Town, South Africa: II-136‒II-139
  22. 22.
    Garzelli A, Nencini F and Capobianco L. 2008. Optimal mmse pansharpening of very high resolution multispectral images. IEEE Transactions on Geoscience and Remote Sensing, 46(1):228-236
  23. 23.
    Gianinetto M, Rusmini M, Candiani G, Via G D, Frassy F, Maianti P, Marchesi A, Nodari FR and Dini L. 2017. Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and obia techniques. European Journal of Remote Sensing, 47(1):229-250
  24. 24.
    Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A C and Bengio Y. 2014. Generative adversarial nets//Proceedings of the Advances in Neural Information Processing Systems (NIPS). Montreal, Quebec, Canada: 2672-2680
  25. 25.
    Guo Y C, Ye F and Gong H. 2019. Learning an efficient convolution neural network for pansharpening. Algorithms, 12(1):16:1-16:14
  26. 26.
    He G Q, Xing S Y, Xia Z Q, Huang Q Q and Fan J P. 2018. Panchromatic and multi-spectral image fusion for new satellites based on multi-channel deep model. Machine Vision and Applications, 29(6):933-946
  27. 27.
    He K M and Sun J. 2015. Convolutional neural networks at constrained time cost//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: 5353-5360
  28. 28.
    He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition. //Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: 770-778
  29. 29.
    He L, Rao Y Z, Li J, Chanussot J, Plaza A, Zhu J W and Li B. 2019. Pansharpening via detail injection based convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(4):1188-1204
  30. 30.
    He L, Xi D H, Li J and Zhu J W. 2020. A spectral-aware convolutional neural network for pansharpening. Applied Sciences, 10(17):5809:1-5809:18
  31. 31.
    Hinton G E and Salakhutdinov R R. 2006. Reducing the dimensionality of data with neural networks. Science, 313:504-507
  32. 32.
    Hinton G E, Osindero S and Teh Y W. 2006. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554
  33. 33.
    Hochreiter S and Schmidhuber J. 1997. Long short-term memory. Neural Computation, 9(8):1735-1780
  34. 34.
    Hu J, He Z and Wu J M. 2019. Deep self-learning network for adaptive pansharpening. Remote Sensing, 11(20):2395:1-2395:22
  35. 35.
    Huang G, Liu Z, Maaten L V D, and Weinberger K Q. 2017. Densely connected convolutional networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: 2261-2269
  36. 36.
    Huang W, Fei X, Yin J R and Liu Y. 2018. A multi-direction subbands and deep neural networks based pan-sharpening method//Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain: 5139-5142
  37. 37.
    Huang W, Xiao L, Wei Z H, Liu H Y and Tang S Z. 2015. A new pan-sharpening method with deep neural networks. IEEE Geoscience and Remote Sensing Letters, 12(5):1037-1041
  38. 38.
    Javan F D, Samadzadegan F, Mehravar S, Toosi A, Khatami R and Stein A. 2021. A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 171:101-117
  39. 39.
    Jiang M H, Li J, Yuan Q Q, Shen H F, Liu X X and Xu M M. 2019. Differential information residual convolutional neural network for pansharpening. // Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan: 4865-4868
  40. 40.
    Kang X D, Li S T and Benediktsson J A. 2014. Pansharpening with matting model. IEEE Transactions on Geoscience and Remote Sensing, 52(8):5088-5099
  41. 41.
    Khan M M, Chanussot J, Condat L and Montanvert A. 2008. Indusion: fusion of multispectral and panchromatic images using the induction scaling technique. IEEE Geoscience and Remote Sensing Letters, 5(1):98-102
  42. 42.
    Kim J, Lee J K and Lee K M. 2016. Accurate image super-resolution using very deep convolutional networks. // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: 1646-1654
  43. 43.
    Laben C A and Brower B V. 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. U.S., No.6011875
  44. 44.
    Lee J, Seo S and Kim M. 2021. Sipsa-net: shift-invariant pan sharpening with moving object alignment for satellite imagery//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Online: 10161-10169
  45. 45.
    Li H, Liu F, Yang S Y and Zhang K. 2016. Remote sensing image fusion based on deep support value learning networks. Chinese Journal of Computers, 39(8): 1583-1596
  46. 46.
    Li J, Li Y F, He L, Chen J and Plaza A. 2020a. Spatio-temporal fusion for remote sensing data: an overview and new benchmark. Science China Information Sciences, 63(4) :7-23
  47. 47.
    Li N, Huang N and Xiao L. 2017. Pan-sharpening via residual deep learning//Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Fort Worth, TX, USA: 5133-5136
  48. 48.
    Li S T，Li C Y and Kang X D. 2021. Development status and future prospects of multi-source remote sensing image fusion. Journal of Remote Sensing, 25(1)：148-166
  49. 49.
    Li S. 2018. Detection Method of Urban Land Cover Change Based on Combining Domain Knowledge and Deep Learning. Wuhan: Wuhan University
  50. 50.
    Li X, Pan Y, Gao A, Li L X, Mei S H and Yue S G. 2018. Pansharpening based on joint gaussian guided upsampling //Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain: 7184-7187
  51. 51.
    Li X, Xu F, Lyu X, Tong Y, Chen Z Q, Li S Y and Liu D F. 2020b. A remote-sensing image pan-sharpening method based on multi-scale channel attention residual network. IEEE Access, 8:27163-27177
  52. 52.
    Li Z Q and Cheng C Q. 2019. A cnn-based pan-sharpening method for integrating panchromatic and multispectral images using landsat 8. Remote Sensing, 11(22): 2606:1-2606:17
  53. 53.
    Liu J M and Liang S L. 2016. Pan-sharpening using a guided filter. International Journal of Remote Sensing, 37(8):1777-1800
  54. 54.
    Liu L, Wang J, Zhang E L, Li B, Zhu X, Zhang Y Q and Peng J Y. 2020. Shallow–deep convolutional network and spectral-discrimination-based detail injection for multispectral imagery pan-sharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13:1772-1783
  55. 55.
    Liu P F, Xiao L and Li T. 2018a. A variational pan-sharpening method based on spatial fractional-order geometry and spectral-spatial low-rank priors. IEEE Transactions on Geoscience and Remote Sensing, 56(3):1788-1802
  56. 56.
    Liu X Y, Wang Y H and Liu Q J. 2018b. Psgan: a generative adversarial network for remote sensing image pan-sharpening//Proceedings of the IEEE International Conference on Image Processing. Athens, Greece: 873-877
  57. 57.
    Liu Y, Chen X, Wang Z F, Wang Z J, Ward R K and Wang X S. 2018c. Deep learning for pixel level image fusion: recent advances and future prospects. Information Fusion, 42:158-173
  58. 58.
    Lu X C, Zhang J P and Zhang Y. 2017. An improved non-subsampled contourlet transform-based hybrid pan-sharpening algorithm//Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Fort Worth, TX, USA: 3393-3396
  59. 59.
    Luo D Q, Huang Y Q, Wang S Z, Fang Y P and Jin W B. 2016. Spectral signature analysis and band selection of Macheng rhododendron based on Landsat5 TM. Hubei Agricultural Sciences, 55(19):4991-4994
  60. 60.
    Ma J Y, Yu W, Chen C, Liang P W, Guo X J and Jiang J J. 2020. Pan-gan: an unsupervised pan-sharpening method for remote sensing image fusion. Information Fusion, 62:110-120
  61. 61.
    Ma L, Liu Y, Zhang X L, Ye Y X, Yin G F and Johnson B A. 2019. Deep learning in remote sensing applications: a meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152:166-177
  62. 62.
    Masi G, Cozzolino D, Verdoliva L and Scarpa G. 2016. Pansharpening by convolutional neural networks. Remote Sensing, 8(7):594:1-594:22
  63. 63.
    Masi G, Cozzolino D, Verdoliva L and Scarpa G. 2017. CNN-based pansharpening of multi-resolution remote-sensing images// Proceedings of the Joint Urban Remote Sensing Event. Dubai, United Arab Emirates: 1-4
  64. 64.
    Mohammadzadeh A, Tavakoli A and Valadan Z M. 2006. Road extraction based on fuzzy logic and mathematical morphology from pan-sharpened ikonos images. The photogrammetric record, 21(113):44-60
  65. 65.
    Otazu X, González-Audícana M, Fors O and Nunez J. 2005. Introduction of sensor spectral response into image fusion methods: application to wavelet-based methods. IEEE Transactions on Geoscience and Remote Sensing, 43(10):2376-2385
  66. 66.
    Ozcelik F, Alganci U, Sertel E and Unal G. 2020. Rethinking cnn-based pansharpening: guided colorization of panchromatic images via gans. IEEE Transactions on Geoscience and Remote Sensing, 59(4):3486-3501
  67. 67.
    Pálsson F, Sveinsson J R, Benediktsson J A and Aanaes H. 2012. Classification of pansharpened Urban satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(1):281-297
  68. 68.
    Palsson F, Sveinsson J R, Ulfarsson M O and Benediktsson J A. 2015. Model based pansharpening method based on tv and mtf deblurring//Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Milan, Italy: 33-36
  69. 69.
    Rao Y Z, He L and Zhu J W. 2017. A residual convolutional neural network for pan-shaprening//2017 International Workshop on Remote Sensing with Intelligent Processing. 1-4
  70. 70.
    Ripley B D. 1996. Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press
  71. 71.
    Scarpa G, Vitale S and Cozzolino D. 2018. Target-adaptive cnn-based pansharpening. IEEE Transactions on Geoscience and Remote Sensing, 56(9):5443-5457
  72. 72.
    Shah V P, Younan N H and King R L. 2008. An efficient pan-sharpening method via a combined adaptive pca approach and contourlets. IEEE Transactions on Geoscience and Remote Sensing, 46(5):1323-1335
  73. 73.
    Shahdoosti H R and Javaheri N. 2017. Pansharpening of clustered ms and pan images considering mixed pixels. IEEE Geoscience and Remote Sensing Letters, 14(6):826-830
  74. 74.
    Shao Z F and Cai J J. 2018. Remote sensing image fusion with deep convolutional neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5):1656-1669
  75. 75.
    Shao Z M, Lu Z X, Ran M S, Fang L Y, Zhou J L and Zhang Y. 2020. Residual encoder-decoder conditional generative adversarial network for pansharpening. IEEE Geoscience and Remote Sensing Letters, 17(9):1573-1577
  76. 76.
    Shi X J, Chen Z R, Wang H, Yeung D Y, Wong W K and Woo W C. 2015. Convolutional lstm network: a machine learning approach for precipitation nowcasting//Proceedings of the International Conference on Neural Information Processing Systems (NIPS). Montreal, Quebec, Canada: 802-810
  77. 77.
    Souza C, Firestone L, Silva L M and Roberts D. 2003. Mapping forest degradation in the eastern amazon from spot 4 through spectral mixture models. Remote Sensing of Environment, 87(4):494-506
  78. 78.
    Tsagkatakis G, Aidini A, Fotiadou K, Giannopoulos M, Pentari A and Tsakalides P. 2019. Survey of deep-learning approaches for remote sensing observation enhancement. Sensors, 19(18):3929:1-3929:39
  79. 79.
    Tu T M, Su S C, Shyu H C and Huang P S. 2001. A new look at IHS-like image fusion methods. Information Fusion, 2(3):177-186
  80. 80.
    Vinothini D S and Bama B S. 2019. Residual dense network for pan-sharpening satellite data. IEEE Sensors Journal, 19(24):12279-12285
  81. 81.
    Vitale S, Ferraioli G and Scarpa G. 2018. A CNN-based model for pansharpening of worldview-3 images//Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain: 5108-5111
  82. 82.
    Vitale S. 2019. A cnn-based pansharpening method with perceptual loss//Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan: 3105-3108
  83. 83.
    Vivone G, Alparone L, Chanussot J, Mura M D, Garzelli A, Licciardi G A, Restaino R and Wald L. 2015. A critical comparison among pansharpening algorithms. IEEE Transactions on Geoscience and Remote Sensing, 53(5):2565-2586
  84. 84.
    Wald L, Ranchin T and Mangolini M. 1997. Fusion of satellite images of different spatial resolutions: assessing the quality of resulting images. Photogrammetric Engineering & Remote Sensing, 63(6):691-699
  85. 85.
    Wald L. 2000. Quality of high resolution synthesised images: Is there a simple criterion? // Proceedings of the Fusion of Earth data: merging point measurements, raster maps and remotely sensed images. Sophia Antipolis, France: 99-103
  86. 86.
    Wang D, Bai Y P and Li Y. 2020. Multispectral pan-sharpening via dual-channel convolutional network with convolutional lstm based hierarchical spatial-spectral feature fusion. ArXiv Preprint ArXiv: 2007.10060
  87. 87.
    Wang F，Guo Q and Ge X Q. 2021. Pan-sharpening by deep recursive residual network. Journal of Remote Sensing, 25(6)：1244-1256
  88. 88.
    Wei Y C and Yuan Q Q. 2017. Deep residual learning for remote sensed imagery pansharpening//Proceedings of the International Workshop on Remote Sensing with Intelligent Processing. Shanghai, China: 1-4
  89. 89.
    Wei Y C, Yuan Q Q, Shen H F and Zhang L P. 2017. Boosting the accuracy of multispectral image pansharpening by learning a deep residual network. IEEE Geoscience and Remote Sensing Letters, 14(10):1795-1799
  90. 90.
    Xiang Z K, Xiao L, Liu P F and Zhang Y F. 2019. A multi-scale densely deep learning method for pansharpening//Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan: 2786-2789
  91. 91.
    Xing Y H, Wang M, Yang S Y and Jiao L C. 2018a. Pan-sharpening via deep metric learning. ISPRS Journal of Photogrammetry and Remote Sensing, 145:165-183
  92. 92.
    Xing Y H, Wang M, Yang S Y and Zhang K. 2018b. Pansharpening with multiscale geometric support tensor machine. IEEE Transactions on Geoscience and Remote Sensing, 56(5):2503-2517
  93. 93.
    Yang J F, Fu X Y, Hu Y W, Huang Y, Ding X H and Paisley J. 2017. Pannet: a deep network architecture for pan-sharpening// Proceedings of the IEEE International Conference on Computer Vision (ICCV). Venice, Italy: 1753-1761
  94. 94.
    Yang S Y, Zhang K and Wang M. 2018a. Learning low-rank decomposition for pansharpening with spatial-spectral offsets. IEEE Transactions on Neural Networks & Learning Systems, 29(8):3647-3657
  95. 95.
    Yang Y, Li L Y, Huang S Y, Zhang Y M and Lu H Y. 2020. Remote sensing image fusion with convolutional sparse representation based on adaptive dictionary learning. Journal of Signal Processing, 36(1):125-138
  96. 96.
    Yang Y, Tu W, Huang S Y and Lu H Y. 2020a. Pcdrn: progressive cascade deep residual network for pansharpening. Remote Sensing, 12(4):676:1-676:20
  97. 97.
    Yang Y, Wu L, Huang S Y, Sun J C, Wan W G and Wu J H. 2018b. Compensation details-based injection model for remote sensing image fusion. IEEE Geoscience and Remote Sensing Letters, 15(5):734-738
  98. 98.
    Yang Y, Wu L, Huang S Y, Tang Y J and Wan W G. 2018c. Pansharpening for multiband images with adaptive spectral–intensity modulation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(9):3196-3208
  99. 99.
    Yang Y, Wu L, Huang S Y, Wan W G, Tu W and Lu H Y. 2020b. Multiband remote sensing image pansharpening based on dual-injection model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13:1888-1904
  100. 100.
    Yao W, Zeng Z G, Lian C and Tang H M. 2018. Pixel-wise regression using u-net and its application on pansharpening. Neurocomputing, 312:364-371
  101. 101.
    Ye F J, Li X F and Zhang X L. 2018. Fusioncnn: a remote sensing image fusion algorithm based on deep convolutional neural networks. Multimedia Tools and Applications, 78(11):14683-14703
  102. 102.
    Ye F, Guo Y C and Zhuang P X. 2019. Pan-sharpening via a gradient-based deep network prior. Signal Processing: Image Communication, 74:322-331
  103. 103.
    Yuan Q Q, Wei Y C, Meng X C, Shen H F and Zhang L P. 2018. A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(3):978-989
  104. 104.
    Zeiler M D and Fergus R. 2014. Visualizing and understanding convolutional networks//Proceedings of the Conference on European Conference on Computer Vision (ECCV). Zurich, Switzerland: 818-833
  105. 105.
    Zhang H Q, Liu X Y, Yang S and Li Y. 2017. Retrieval of remote sensing images based on semisupervised deep learning. Journal of Remote Sensing, 21(3): 406-414
  106. 106.
    Zhang L B, Zhang J, Lyu X R and Ma J. 2019a. A new pansharpening method using objectness based saliency analysis and saliency guided deep residual network//Proceedings of the IEEE International Conference on Image Processing. Taipei, China: 4529-4533
  107. 107.
    Zhang L P, Li W S, Zhang C and Lei D J. 2020. A generative adversarial network with structural enhancement and spectral supplement for pan-sharpening. Neural Computing and Applications, 32(24):18347-18359
  108. 108.
    Zhang Y J, Liu C, Sun M W and Ou Y J. 2019b. Pan-sharpening using an efficient bidirectional pyramid network. IEEE Transactions on Geoscience and Remote Sensing, 57(8):5549-5563
  109. 109.
    Zhang Y L, Tian Y P, Kong Y, Zhong B E and Fu Y. 2018. Residual dense network for image super-resolution//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, UT, USA: 2472-2481
  110. 110.
    Zhang Y T, Li X H, Zhou JL. 2019c. Sftgan: a generative adversarial network for pan-sharpening equipped with spatial feature transform layers. Journal of Applied Remote Sensing, 13(2): 026507:1-026507:16
  111. 111.
    Zhong J Y, Yang B, Huang G Y, Zhong Fei and Chen Z Z. 2016. Remote sensing image fusion with convolutional neural network. Sensing and Imaging, 17(1):6:1-6:16
  112. 112.
    Zhou M, Yan K Y, Huang J, Yang Z H, Fu X Y and Zhao F. 2022. Mutual information-driven pan-sharpening//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans, Louisiana, USA: 1788-1798

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