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Lihui-Chen / Awesome-Pansharpening

This repository collects pan-sharpening methods, codes, and datasets.

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Awesome-Pansharpening

This repository collects pan-sharpening methods (focus on deep learning based methods), codes, and datasets. If you find some interesting papers not included, please feel free to contact me.

Contents

  1. Datasets

  2. Survey

  3. Performance Assessment

  4. CS-based Methods

  5. MRA-based Methods

  6. [MO-based Methods](#5Model Optimization Based Pansharpening)

  7. [DL-based Methods](#Deep Learning Based Pansharpening)

  8. [Challenges](#Challenges In Pansharpening)

Datasets

  1. 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.

    NBU-Dataset (Password：y77y)

  2. G. Vivone, M. Dalla Mura, A. Garzelli, and F. Pacifici, "A Benchmarking Protocol for Pansharpening: Dataset, Pre-processing, and Quality Assessment," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021.

    PAirMax benchmark dataset

  3. Liangjian Deng et al., PanCollection Dataset

    A fully prepared dataset with training and test sets by the pipeline described in the following paper.

     @ARTICLE{dengjig2022,
     author={邓良剑，冉燃，吴潇，张添敬},
     journal={**图象图形学报},
     title={遥感图像全色锐化的卷积神经网络方法研究进展},
     year={2022},
     volume={},
     number={9},
     pages={},
     doi={10.11834/jig.220540}
 }

  4. Yu-Wei Zhuo, Tian-Jing Zhang, Jin-Fan Hu, Hong-Xia Dou, Ting-Zhu Huang, Liang-Jian Deng*, "A Deep-Shallow Fusion Network With Multidetail Extractor and Spectral Attention for Hyperspectral Pansharpening," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022. Hyperspectral Pansharpening Dataset (A easy-to-use dataset "HyperPanCollection" for hyperspectral pansharpening)

  5. Remote Sensing Product Samples

  6. gscloud

Survey

  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.
  10. Liang-Jian Deng, Gemine Vivone*, Mercedes E. Paoletti, Giuseppe Scarpa, Jiang He, Yongjun Zhang, Jocelyn Chanussot, Antonio Plaza, "Machine Learning in Pansharpening: A Benchmark, from Shallow to Deep Networks," IEEE Geoscience and Remote Sensing Magazine, 2022 (A bechmark and easy-to-use code toolbox for pansharpening) [Codes]

Performance-Assessment

[1] R. H. Yuhas, A. F. Goetz, and J. W. Boardman, “Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm,” in Proc. Summaries 3rd Annu. JPL Airborne Geosci. Workshop, 1992, vol. 1, pp. 147–149.

[2] 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.

[3] J. Zhou, D. L. Civco, and J. A. Silander, “A wavelet transform method to merge Landsat TM and SPOT panchromatic data,” International Journal of Remote Sensing, vol. 19, no. 4, pp. 743–757, Jan. 1998, doi: 10.1080/014311698215973.

[4] L. Wald, Data fusion: definitions and architectures: fusion of images of different spatial resolutions. Presses des MINES, 2002.

[5]Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81–84, Mar. 2002, doi: 10.1109/97.995823.

[6] 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.

[7] 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.

[8] C. Thomas and L. Wald, “Comparing distances for quality assessment of fused images,” in 26th EARSeL Symposium, Varsovie, Poland, May 2006, pp. 101–111. Accessed: Jun. 18, 2021. [Online]. Available: https://hal.archives-ouvertes.fr/hal-00395062

[9] 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.

[10] L. Alparone, B. Aiazzi, S. Baronti, A. Garzelli, F. Nencini, and M. Selva, “Multispectral and Panchromatic Data Fusion Assessment Without Reference,” Photogrammetric Engineering & Remote Sensing, vol. 74, no. 2, pp. 193–200, Feb. 2008, doi: 10.14358/PERS.74.2.193.

[11] A. Garzelli and F. Nencini, “Hypercomplex Quality Assessment of Multi/Hyperspectral Images,” IEEE Geoscience and Remote Sensing Letters, vol. 6, no. 4, pp. 662–665, Oct. 2009, doi: 10.1109/LGRS.2009.2022650.

[12] 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.

[13] K. Kotwal and S. Chaudhuri, “A novel approach to quantitative evaluation of hyperspectral image fusion techniques,” Information Fusion, vol. 14, no. 1, pp. 5–18, Jan. 2013, doi: 10.1016/j.inffus.2011.03.008.

[14] 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.

[15] X. Huang, D. Wen, J. Xie, and L. Zhang, “Quality Assessment of Panchromatic and Multispectral Image Fusion for the ZY-3 Satellite: From an Information Extraction Perspective,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 4, pp. 753–757, Apr. 2014, doi: 10.1109/LGRS.2013.2278551.

[16] G. Vivone, R. Restaino, M. Dalla Mura, G. Licciardi, and J. Chanussot, “Contrast and Error-Based Fusion Schemes for Multispectral Image Pansharpening,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 5, pp. 930–934, May 2014, doi: 10.1109/LGRS.2013.2281996.

[17] 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.

[18] G. Palubinskas, “Joint Quality Measure for Evaluation of Pansharpening Accuracy,” Remote Sensing, vol. 7, no. 7, pp. 9292–9310, Jul. 2015, doi: 10.3390/rs70709292.

[19] C. Kwan, B. Budavari, A. C. Bovik, and G. Marchisio, “Blind Quality Assessment of Fused WorldView-3 Images by Using the Combinations of Pansharpening and Hypersharpening Paradigms,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 10, pp. 1835–1839, Oct. 2017, doi: 10.1109/LGRS.2017.2737820.

[20] L. Alparone, A. Garzelli, and G. Vivone, “Spatial Consistency for Full-Scale Assessment of Pansharpening,” in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Jul. 2018, pp. 5132–5134. doi: 10.1109/IGARSS.2018.8518869.

[21] 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.

[22] 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.

[23] 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.

[24] 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.

[25] Scarpa G, Ciotola M. Full-Resolution Quality Assessment for Pansharpening. Remote Sensing. 2022; 14(8):1808. https://doi.org/10.3390/rs14081808. paper

Component Substitute (CS)-Based Pansharpening

  1. W. Carper, T. Lillesand, and R. Kiefer. 1990. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering and remote sensing 56, 4 (1990), 459–467.
  2. Alan R Gillespie, Anne B Kahle, and Richard E Walker. 1987. Color enhancement of highly correlated images. II. Channel ratio and “chromaticity” transformation techniques. Remote Sensing of Environment 22, 3 (1987), 343–365.
  3. Craig A Laben and Bernard V Brower. 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. US Patent 6,011,875.
  4. Sheida Rahmani, Melissa Strait, Daria Merkurjev, Michael Moeller, and Todd Wittman. 2010. An adaptive IHS pan-sharpening method. IEEE Geoscience and Remote Sensing Letters 7, 4 (2010), 746–750

Multi Resolution Analysis (MRA)-Based Pansharpening

  1. Bruno Aiazzi, Luciano Alparone, Stefano Baronti, and Andrea Garzelli. 2002. Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis. IEEE Transactions on geoscience and remote sensing 40, 10 (2002), 2300–2312
  2. Xavier Otazu, María González-Audícana, Octavi Fors, and Jorge Núñez. 2005. Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods. IEEE Transactions on Geoscience and Remote Sensing 43, 10 (2005), 2376–2385.
  3. Vijay P Shah, Nicolas H Younan, and Roger L King. 2008. An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets. IEEE transactions on geoscience and remote sensing 46, 5 (2008), 1323–1335.
  4. Zhijun Wang, Djemel Ziou, Costas Armenakis, Deren Li, and Qingquan Li. 2005. A comparative analysis of image fusion methods. IEEE transactions on geoscience and remote sensing 43, 6 (2005), 1391–1402.

Model Optimization Based Pansharpening

  1. Xueyang Fu, Zihuang Lin, Yue Huang, and Xinghao Ding. 2019. A Variational Pan-Sharpening With Local Gradient Constraints. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 10257–10266. https://doi.org/10.1109/CVPR.2019.01051
  2. Pengfei Liu, Liang Xiao, and Tao Li. 2018. 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 (March 2018), 1788–1802. https://doi.org/10.1109/TGRS.2017.2768386
  3. Penghao Guo, Peixian Zhuang, and Yecai Guo. 2020. Bayesian Pan-Sharpening With Multiorder Gradient-Based Deep Network Constraints. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020), 950–962. https://doi.org/10.1109/JSTARS.2020.2975000
  4. Haitao Yin. 2019. PAN-Guided Cross-Resolution Projection for Local Adaptive Sparse Representation- Based Pansharpening. IEEE Transactions on Geoscience and Remote Sensing 57 (July 2019), 4938–4950. https://doi.org/10.1109/TGRS.2019.2894702
  5. L. Yu, D. Liu, H. Mansour and P. T. Boufounos, "Fast and High-Quality Blind Multi-Spectral Image Pansharpening," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-17, 2022, Art no. 5403417, doi: 10.1109/TGRS.2021.3091329.paper

Deep Learning Based Pansharpening

Supervised Methods

  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.
  38. 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.
  39. S. Xu, J. Zhang, Z. Zhao, K. Sun, J. Liu, and C. Zhang, “Deep Gradient Projection Networks for Pan-sharpening,” CVPR2021, Mar. 2021.
  40. X. Wu, T.-Z. Huang, L.-J. Deng, and T.-J. Zhang, “Dynamic Cross Feature Fusion for Remote Sensing Pansharpening,” ICCV2021, [paper].
  41. C. Jin, L.-J. Deng, T.-Z. Huang, and G. Vivone, “Laplacian pyramid networks: A new approach for multispectral pansharpening,” Information Fusion, vol. 78, pp. 158–170, Feb. 2022, paper [codes].
  42. Y. Wang, L.-J. Deng, T.-J. Zhang, and X. Wu, “SSconv: Explicit Spectral-to-Spatial Convolution for Pansharpening,” in Proceedings of the 29th ACM International Conference on Multimedia, Virtual Event China, Oct. 2021, pp. 4472–4480. [paper][codes].
  43. WU Z C, HUANG T Z, DENG L J, 等. VO+Net: An Adaptive Approach Using Variational Optimization and Deep Learning for Panchromatic Sharpening[J/OL]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-16. DOI:10.1109/TGRS.2021.3066425.
  44. CIOTOLA M, VITALE S, MAZZA A, 等. Pansharpening by convolutional neural networks in the full resolution framework[J/OL]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-17. DOI:10.1109/TGRS.2022.3163887.
  45. DIAO W, ZHANG F, WANG H, 等. HLF-Net: Pansharpening Based on High- and Low-Frequency Fusion Networks[J/OL]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-5. DOI:10.1109/LGRS.2022.3225974.

Unsupervised Methods

  1. S. Luo, S. Zhou, Y. Feng, and J. Xie, “Pansharpening via Unsupervised Convolutional Neural Networks,” IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, vol. 13, p. 16, 2020.
  2. J. Ma, W. Yu, C. Chen, P. Liang, X. Guo, and J. Jiang, “Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion,” Information Fusion, vol. 62, pp. 110–120, Oct. 2020, doi: 10.1016/j.inffus.2020.04.006.
  3. Y. Qu, R. K. Baghbaderani, H. Qi, and C. Kwan, “Unsupervised Pansharpening Based on Self-Attention Mechanism,” IEEE Trans. Geosci. Remote Sensing, pp. 1–17, 2020, doi: 10.1109/TGRS.2020.3009207.
  4. T. Uezato, D. Hong, N. Yokoya, and W. He, “Guided Deep Decoder: Unsupervised Image Pair Fusion,” in Computer Vision – ECCV 2020, vol. 12351, A. Vedaldi, H. Bischof, T. Brox, and J.-M. Frahm, Eds. Cham: Springer International Publishing, 2020, pp. 87–102. doi: 10.1007/978-3-030-58539-6_6.
  5. C. Zhou, J. Zhang, J. Liu, C. Zhang, R. Fei, and S. Xu, “PercepPan: Towards Unsupervised Pan-Sharpening Based on Perceptual Loss,” p. 22, 2020.
  6. M. Ciotola, S. Vitale, A. Mazza, G. Poggi and G. Scarpa, "Pansharpening by Convolutional Neural Networks in the Full Resolution Framework," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-17, 2022, Art no. 5408717, doi: 10.1109/TGRS.2022.3163887. paper
  7. Ciotola M, Scarpa G. Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening Framework. Remote Sensing. 2023; 15(2):319. https://doi.org/10.3390/rs15020319. paper

Challenges In Pansharpening

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This repository collects pan-sharpening methods, codes, and datasets.