This repository collects pan-sharpening methods (focus on deep learning based methods), codes, and datasets.

1. Survey
2. Performance Assessment
3. CS-based Methods
4. MRA-based Methods
5. MO-based Methods
6. DL-based Methods
   • Supervised Methods
   • Unsupervised Methods
7. Challenges

1. F. Laporterie-Déjean, H. de Boissezon, G. Flouzat, and M.-J. Lefèvre-Fonollosa, “Thematic and statistical evaluations of five panchromatic/multispectral fusion methods on simulated PLEIADES-HR images,” Information Fusion, vol. 6, no. 3, pp. 193–212, Sep. 2005, doi: 10.1016/j.inffus.2004.06.006.
2. C. Thomas, T. Ranchin, L. Wald, and J. Chanussot, “Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics,” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 5, pp. 1301–1312, May 2008, doi: 10.1109/TGRS.2007.912448.
3. J. Marcello, A. Medina, and F. Eugenio, “Evaluation of Spatial and Spectral Effectiveness of Pixel-Level Fusion Techniques,” IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 3, pp. 432–436, May 2013, doi: 10.1109/LGRS.2012.2207944.
4. K. Kpalma, M. C. El-Mezouar, and N. Taleb, “Recent Trends in Satellite Image Pan-sharpening techniques,” 1st International Conference on Electrical, Electronic and Computing Engineering, Jun 2014, Vrniacka Banja, Serbia. ffhal-01075703
5. G. Vivone et al., “A Critical Comparison Among Pansharpening Algorithms,” IEEE Trans. Geosci. Remote Sensing, vol. 53, no. 5, pp. 2565–2586, May 2015, doi: 10.1109/TGRS.2014.2361734. [codes]
6. L. Loncan et al., “Hyperspectral Pansharpening: A Review,” IEEE Geosci. Remote Sens. Mag., vol. 3, no. 3, pp. 27–46, Sep. 2015, doi: 10.1109/MGRS.2015.2440094.
7. X. Meng, H. Shen, H. Li, L. Zhang, and R. Fu, “Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: Practical discussion and challenges,” Information Fusion, vol. 46, pp. 102–113, Mar. 2019, doi: 10.1016/j.inffus.2018.05.006.
8. G. Vivone et al., “A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods,” IEEE Geosci. Remote Sens. Mag., vol. 9, no. 1, pp. 53–81, Mar. 2021, doi: 10.1109/MGRS.2020.3019315.
9. X. Meng et al., “A Large-Scale Benchmark Data Set for Evaluating Pansharpening Performance: Overview and Implementation,” IEEE Geosci. Remote Sens. Mag., vol. 9, no. 1, pp. 18–52, Mar. 2021, doi: 10.1109/MGRS.2020.2976696.

1. L. Wald, T. Ranchin, and M. Mangolini, “Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images,” Photogrammetric engineering and remote sensing, vol. 63, no. 6, pp. 691–699, 1997.

2. L. Alparone, S. Baronti, A. Garzelli, and F. Nencini, “A global quality measurement of pan-sharpened multispectral imagery,” IEEE Geoscience and Remote Sensing Letters, vol. 1, no. 4, pp. 313–317, Oct. 2004, doi: 10.1109/LGRS.2004.836784.

3. C. Thomas and L. Wald, “Analysis of Changes in Quality Assessment with Scale,” in 2006 9th International Conference on Information Fusion, Jul. 2006, pp. 1–5. doi: 10.1109/ICIF.2006.301595.

4. Q. Du, N. H. Younan, R. King, and V. P. Shah, “On the Performance Evaluation of Pan-Sharpening Techniques,” IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 4, pp. 518–522, Oct. 2007, doi: 10.1109/LGRS.2007.896328.

5. L. Alparone, B. Aiazzi, S. Baronti, A. Garzelli, F. Nencini, and M. Selva, “Multispectral and Panchromatic Data Fusion Assessment Without Reference,” PHOTOGRAMMETRIC ENGINEERING, Vol. 74, No. 2, February 2008, pp. 193–200.

6. M. M. Khan, L. Alparone, and J. Chanussot, “Pansharpening Quality Assessment Using the Modulation Transfer Functions of Instruments,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 11, pp. 3880–3891, Nov. 2009, doi: 10.1109/TGRS.2009.2029094.

7. B. Aiazzi, L. Alparone, S. Baronti, R. Carlà, A. Garzelli, and L. Santurri, “Full-scale assessment of pansharpening methods and data products,” in Image and Signal Processing for Remote Sensing XX, Oct. 2014, vol. 9244, p. 924402. doi: 10.1117/12.2067770.

8. R. Carla, L. Santurri, B. Aiazzi, and S. Baronti, “Full-Scale Assessment of Pansharpening Through Polynomial Fitting of Multiscale Measurements,” IEEE Trans. Geosci. Remote Sensing, vol. 53, no. 12, pp. 6344–6355, Dec. 2015, doi: 10.1109/TGRS.2015.2436699.

9. W. Dou, “Image Degradation for Quality Assessment of Pan-Sharpening Methods,” Remote Sensing, vol. 10, no. 2, p. 154, Jan. 2018, doi: 10.3390/rs10010154.

10. M. Selva, L. Santurri, and S. Baronti, “On the Use of the Expanded Image in Quality Assessment of Pansharpened Images,” IEEE Geosci. Remote Sensing Lett., vol. 15, no. 3, pp. 320–324, Mar. 2018, doi: 10.1109/LGRS.2017.2777916.

11. G. Vivone, R. Restaino, and J. Chanussot, “A Bayesian Procedure for Full-Resolution Quality Assessment of Pansharpened Products,” IEEE Trans. Geosci. Remote Sensing, vol. 56, no. 8, pp. 4820–4834, Aug. 2018, doi: 10.1109/TGRS.2018.2839564.

12. O. A. Agudelo-Medina, H. D. Benitez-Restrepo, G. Vivone, and A. Bovik, “Perceptual Quality Assessment of Pan-Sharpened Images,” Remote Sensing, vol. 11, no. 7, p. 877, Apr. 2019, doi: 10.3390/rs11070877.

1. Wei Huang, Liang Xiao, Zhihui Wei, Hongyi Liu, and Songze Tang, “A New Pan-Sharpening Method With Deep Neural Networks,” IEEE Geosci. Remote Sensing Lett., vol. 12, no. 5, pp. 1037–1041, May 2015, doi: 10.1109/LGRS.2014.2376034.
2. G. Masi, D. Cozzolino, L. Verdoliva, and G. Scarpa, “Pansharpening by Convolutional Neural Networks,” Remote Sensing, vol. 8, no. 7, Art. no. 7, Jul. 2016, doi: 10.3390/rs8070594.
3. A. Azarang and H. Ghassemian, “A new pansharpening method using multi resolution analysis framework and deep neural networks,” in 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), Shahrekord, Iran, Apr. 2017, pp. 1–6. doi: 10.1109/PRIA.2017.7983017.
4. N. Li, N. Huang, and L. Xiao, “PAN-Sharpening via residual deep learning,” in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul. 2017, pp. 5133–5136. doi: 10.1109/IGARSS.2017.8128158.
5. G. Masi, D. Cozzolino, L. Verdoliva, and G. Scarpa, “CNN-based pansharpening of multi-resolution remote-sensing images,” in 2017 Joint Urban Remote Sensing Event (JURSE), Dubai, United Arab Emirates, Mar. 2017, pp. 1–4. doi: 10.1109/JURSE.2017.7924534.
6. Y. Wei and Q. Yuan, “Deep residual learning for remote sensed imagery pansharpening,” in 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China, May 2017, pp. 1–4. doi: 10.1109/RSIP.2017.7958794.
7. Y. Wei, Q. Yuan, H. Shen, and L. Zhang, “Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network,” IEEE Geosci. Remote Sensing Lett., vol. 14, no. 10, pp. 1795–1799, Oct. 2017, doi: 10.1109/LGRS.2017.2736020.
8. J. Yang, X. Fu, Y. Hu, Y. Huang, X. Ding, and J. Paisley, “PanNet: A Deep Network Architecture for Pan-Sharpening,” ICCV2017, https://openaccess.thecvf.com/content_iccv_2017/html/Yang_PanNet_A_Deep_ICCV_2017_paper.html
9. X. Liu, Y. Wang, and Q. Liu, “Psgan: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening,” in 2018 25th IEEE International Conference on Image Processing (ICIP), Oct. 2018, pp. 873–877. doi: 10.1109/ICIP.2018.8451049.
10. G. Scarpa, S. Vitale, and D. Cozzolino, “Target-Adaptive CNN-Based Pansharpening,” IEEE Trans. Geosci. Remote Sensing, vol. 56, no. 9, pp. 5443–5457, Sep. 2018, doi: 10.1109/TGRS.2018.2817393.
11. S. Vitale, G. Ferraioli, and G. Scarpa, “A CNN-Based Model for Pansharpening of WorldView-3 Images,” in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Jul. 2018, pp. 5108–5111. doi: 10.1109/IGARSS.2018.8519202.
12. Q. Yuan, Y. Wei, X. Meng, H. Shen, and L. Zhang, “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, vol. 11, no. 3, pp. 978–989, Mar. 2018, doi: 10.1109/JSTARS.2018.2794888.
13. K. Doi and A. Iwasaki, “SSCNET: Spectral-Spatial Consistency Optimization of CNN for Pansharpening,” in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Jul. 2019, pp. 3141–3144. doi: 10.1109/IGARSS.2019.8897928.
14. F. Palsson, J. R. Sveinsson, and M. O. Ulfarsson, “Optimal Component Substitution and Multi-Resolution Analysis Pansharpening Methods Using a Convolutional Neural Network,” in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, Jul. 2019, pp. 3177–3180. doi: 10.1109/IGARSS.2019.8899299.
15. S. Vitale, “A CNN-Based Pansharpening Method with Perceptual Loss,” in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, Jul. 2019, pp. 3105–3108. doi: 10.1109/IGARSS.2019.8900390.
16. Z. Xiang, L. Xiao, P. Liu, and Y. Zhang, “A Multi-Scale Densely Deep Learning Method for Pansharpening,” in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, Jul. 2019, pp. 2786–2789. doi: 10.1109/IGARSS.2019.8898095.
17. L. Zhang, J. Zhang, X. Lyu, and J. Ma, “A New Pansharpening Method Using Objectness Based Saliency Analysis and Saliency Guided Deep Residual Network,” in 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, Sep. 2019, pp. 4529–4533. doi: 10.1109/ICIP.2019.8803477.
18. Y. Zhang, C. Liu, M. Sun, and Y. Ou, “Pan-Sharpening Using an Efficient Bidirectional Pyramid Network,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 8, pp. 5549–5563, Aug. 2019, doi: 10.1109/TGRS.2019.2900419.
19. Y. Zheng, J. Li, and Y. Li, “Hyperspectral Pansharpening Based on Guided Filter and Deep Residual Learning,” in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, Jul. 2019, pp. 616–619. doi: 10.1109/IGARSS.2019.8899015.
20. J. Cai and B. Huang, “Super-Resolution-Guided Progressive Pansharpening Based on a Deep Convolutional Neural Network,” IEEE Trans. Geosci. Remote Sensing, pp. 1–15, 2020, doi: 10.1109/TGRS.2020.3015878.
21. J.-S. Choi, Y. Kim, and M. Kim, “S3: A Spectral-Spatial Structure Loss for Pan-Sharpening Networks,” IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 5, pp. 829–833, May 2020, doi: 10.1109/LGRS.2019.2934493.
22. L.-J. Deng, G. Vivone, C. Jin, and J. Chanussot, “Detail Injection-Based Deep Convolutional Neural Networks for Pansharpening,” IEEE Trans. Geosci. Remote Sensing, pp. 1–16, 2020, doi: 10.1109/TGRS.2020.3031366.
23. S. Fu, W. Meng, G. Jeon, A. Chehri, R. Zhang, and X. Yang, “Two-Path Network with Feedback Connections for Pan-Sharpening in Remote Sensing,” Remote Sensing, vol. 12, no. 10, p. 1674, May 2020, doi: 10.3390/rs12101674.
24. A. Gastineau, J.-F. Aujol, Y. Berthoumieu, and C. Germain, “A Residual Dense Generative Adversarial Network For Pansharpening With Geometrical Constraints,” in 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, Oct. 2020, pp. 493–497. doi: 10.1109/ICIP40778.2020.9191230.
25. P. Guo, P. Zhuang, and Y. Guo, “Bayesian Pan-Sharpening With Multiorder Gradient-Based Deep Network Constraints,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 950–962, 2020, doi: 10.1109/JSTARS.2020.2975000.
26. J. Hu, P. Hu, X. Kang, H. Zhang, and S. Fan, “Pan-Sharpening via Multiscale Dynamic Convolutional Neural Network,” IEEE Transactions on Geoscience and Remote Sensing, pp. 1–14, 2020, doi: 10.1109/TGRS.2020.3007884.
27. C. Liu et al., “Band-Independent Encoder–Decoder Network for Pan-Sharpening of Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 7, pp. 5208–5223, Jul. 2020, doi: 10.1109/TGRS.2020.2975230.
28. J. Liu, Y. Feng, C. Zhou, and C. Zhang, “PWNet: An Adaptive Weight Network for the Fusion of Panchromatic and Multispectral Images,” Remote Sensing, vol. 12, no. 17, p. 2804, Aug. 2020, doi: 10.3390/rs12172804.
29. L. Liu et al., “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, vol. 13, pp. 1772–1783, 2020, doi: 10.1109/JSTARS.2020.2981695.
30. X. Liu, Q. Liu, and Y. Wang, “Remote sensing image fusion based on two-stream fusion network,” Information Fusion, vol. 55, pp. 1–15, Mar. 2020, doi: 10.1016/j.inffus.2019.07.010.
31. F. Ozcelik, U. Alganci, E. Sertel, and G. Unal, “Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs,” IEEE Trans. Geosci. Remote Sensing, pp. 1–16, 2020, doi: 10.1109/TGRS.2020.3010441.
32. Z. Shao, Z. Lu, M. Ran, L. Fang, J. Zhou, and Y. Zhang, “Residual Encoder–Decoder Conditional Generative Adversarial Network for Pansharpening,” IEEE Geosci. Remote Sensing Lett., vol. 17, no. 9, pp. 1573–1577, Sep. 2020, doi: 10.1109/LGRS.2019.2949745.
33. S. Vitale and G. Scarpa, “A Detail-Preserving Cross-Scale Learning Strategy for CNN-Based Pansharpening,” Remote Sensing, vol. 12, no. 3, p. 348, Jan. 2020, doi: 10.3390/rs12030348.
34. W. Wei and Y. Zhang, “Deep Recursive Network for Hyperspectral Image Super-Resolution,” IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, vol. 6, p. 12, 2020.
35. Y. Yang, W. Tu, S. Huang, and H. Lu, “PCDRN: Progressive Cascade Deep Residual Network for Pansharpening,” Remote Sensing, vol. 12, no. 4, p. 676, Feb. 2020, doi: 10.3390/rs12040676.
36. Y. Zheng, J. Li, Y. Li, K. Cao, and K. Wang, “Deep Residual Learning for Boosting the Accuracy of Hyperspectral Pansharpening,” IEEE Geosci. Remote Sensing Lett., vol. 17, no. 8, pp. 1435–1439, Aug. 2020, doi: 10.1109/LGRS.2019.2945424.
37. Y. Zheng, J. Li, Y. Li, J. Guo, X. Wu, and J. Chanussot, “Hyperspectral Pansharpening Using Deep Prior and Dual Attention Residual Network,” IEEE Trans. Geosci. Remote Sensing, vol. 58, no. 11, pp. 8059–8076, Nov. 2020, doi: 10.1109/TGRS.2020.2986313.
39. D. Lei, H. Chen, L. Zhang, and W. Li, “NLRNet: An Efficient Nonlocal Attention ResNet for Pansharpening,” IEEE Transactions on Geoscience and Remote Sensing, pp. 1–13, 2021, doi: 10.1109/TGRS.2021.3067097.
40. S. Xu, J. Zhang, Z. Zhao, K. Sun, J. Liu, and C. Zhang, “Deep Gradient Projection Networks for Pan-sharpening,” CVPR2021, Mar. 2021,

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