yc-cui / Pansharpening-Zoo

A collection of deep learning based pansharpening models.

Pansharpening-Zoo

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Supervised

• [ICCV 2017 PanNet] [paper] [code] J. Yang, X. Fu, Y. Hu, Y. Huang, X. Ding and J. Paisley, "PanNet: A Deep Network Architecture for Pan-Sharpening," 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp. 1753-1761, doi: 10.1109/ICCV.2017.193.

  

• [CVPR 2021 SIPSA-Net] [paper] [code] J. Lee, S. Seo and M. Kim, "SIPSA-Net: Shift-Invariant Pan Sharpening with Moving Object Alignment for Satellite Imagery," 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021, pp. 10161-10169, doi: 10.1109/CVPR46437.2021.01003.

  

• [ECCV 2022 MMNet] [paper] [code] Yan, K., Zhou, M., Zhang, L., Xie, C. (2022). Memory-Augmented Model-Driven Network for Pansharpening. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13679. Springer, Cham. https://doi.org/10.1007/978-3-031-19800-7_18

  

• [CVPR 2023 PGCU] [paper] [code] Z. Zhu, X. Cao, M. Zhou, J. Huang, and D. Meng, “Probability-Based Global Cross-Modal Upsampling for Pansharpening,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2023, pp. 14039–14048.

  

• [TGRS 2021 PSGAN] [paper] [code] Q. Liu, H. Zhou, Q. Xu, X. Liu and Y. Wang, "PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 12, pp. 10227-10242, Dec. 2021, doi: 10.1109/TGRS.2020.3042974.

  

• [CVPR 2022 HyperTransformer] [paper] [code] W. G. C. Bandara and V. M. Patel, "HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 1757-1767, doi: 10.1109/CVPR52688.2022.00181.

  

• [CVPR 2021 GPPNN] [paper] [code] S. Xu, J. Zhang, Z. Zhao, K. Sun, J. Liu and C. Zhang, "Deep Gradient Projection Networks for Pan-sharpening," 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021, pp. 1366-1375, doi: 10.1109/CVPR46437.2021.00142.

  

• [TGRS 2022 DIP-HyperKite] [paper] [code] W. G. C. Bandara, J. M. J. Valanarasu and V. M. Patel, "Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022, Art no. 5520816, doi: 10.1109/TGRS.2021.3139292.

  

• [CVPR 2022 MDCUN] [paper] [code] [code] G. Yang, M. Zhou, K. Yan, A. Liu, X. Fu and F. Wang, "Memory-augmented Deep Conditional Unfolding Network for Pansharpening," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 1778-1787, doi: 10.1109/CVPR52688.2022.00183.

  

• [CVPR 2022 MutInf] [paper] [code] [code] M. Zhou, K. Yan, J. Huang, Z. Yang, X. Fu and F. Zhao, "Mutual Information-driven Pan-sharpening," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 1788-1798, doi: 10.1109/CVPR52688.2022.00184.

  

• [AAAI 2022 panformer] [paper] [code] Zhou, M., Huang, J., Fang, Y., Fu, X., & Liu, A. (2022). Pan-Sharpening with Customized Transformer and Invertible Neural Network. Proceedings of the AAAI Conference on Artificial Intelligence, 36(3), 3553-3561. https://doi.org/10.1609/aaai.v36i3.20267

  

• [ICCV 2021 DCFNet] [paper] [code] X. Wu, T. -Z. Huang, L. -J. Deng and T. -J. Zhang, "Dynamic Cross Feature Fusion for Remote Sensing Pansharpening," 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp. 14667-14676, doi: 10.1109/ICCV48922.2021.01442.

  

• [ECCV 2022 SFIIN] [paper] [code] Zhou, M. et al. (2022). Spatial-Frequency Domain Information Integration for Pan-Sharpening. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13678. Springer, Cham. https://doi.org/10.1007/978-3-031-19797-0_16

  

• [CVPR 2019 MHF-net] [paper] [code] Q. Xie, M. Zhou, Q. Zhao, D. Meng, W. Zuo and Z. Xu, "Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 1585-1594, doi: 10.1109/CVPR.2019.00168.

  

• [J-STARS 2020 MIPSM] [paper] [code] L. Liu et al., "Shallow–Deep Convolutional Network and Spectral-Discrimination-Based Detail Injection for Multispectral Imagery Pan-Sharpening," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 1772-1783, 2020, doi: 10.1109/JSTARS.2020.2981695.

  

• [TNNLS 2021] [paper] [code] X. Fu, W. Wang, Y. Huang, X. Ding and J. Paisley, "Deep Multiscale Detail Networks for Multiband Spectral Image Sharpening," in IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 2090-2104, May 2021, doi: 10.1109/TNNLS.2020.2996498.

  

• [TGRS 2020 FusionNet] [paper] [code] L. -J. Deng, G. Vivone, C. Jin and J. Chanussot, "Detail Injection-Based Deep Convolutional Neural Networks for Pansharpening," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 8, pp. 6995-7010, Aug. 2021, doi: 10.1109/TGRS.2020.3031366.

  

• [RS 2016 PNN] [paper] [code] Masi, G.; Cozzolino, D.; Verdoliva, L.; Scarpa, G. Pansharpening by Convolutional Neural Networks. Remote Sens. 2016, 8, 594. https://doi.org/10.3390/rs8070594

  

• [TGRS 2022 DR-Net] [paper] [code] X. Su, J. Li and Z. Hua, "Transformer-Based Regression Network for Pansharpening Remote Sensing Images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-23, 2022, Art no. 5407423, doi: 10.1109/TGRS.2022.3152425.

  

• [ICME 2022 PanFormer] [paper] [code] H. Zhou, Q. Liu and Y. Wang, "PanFormer: A Transformer Based Model for Pan-Sharpening," 2022 IEEE International Conference on Multimedia and Expo (ICME), Taipei, Taiwan, 2022, pp. 1-6, doi: 10.1109/ICME52920.2022.9859770.

  

• [CVPRW 2022 DII] [paper] [code] J. Gao, J. Li, X. Su, M. Jiang and Q. Yuan, "Deep Image Interpolation: A Unified Unsupervised Framework for Pansharpening," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, LA, USA, 2022, pp. 608-617, doi: 10.1109/CVPRW56347.2022.00076.

  

• [TGRS 2023 FAFNet] [paper] [code] Y. Xing, Y. Zhang, H. He, X. Zhang and Y. Zhang, "Pansharpening via Frequency-Aware Fusion Network With Explicit Similarity Constraints," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-14, 2023, Art no. 5403614, doi: 10.1109/TGRS.2023.3281829.

  

• [TGRS 2023 PSCF-Net] [paper] [code] S. Peng, D. Zhu, Q. Gao, Y. Lu and D. Sun, "PSCF-Net: Deeply Coupled Feedback Network for Pansharpening," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-12, 2023, Art no. 5401812, doi: 10.1109/TGRS.2023.3261386.

  

• [TGRS 2023 PSDNet] [paper] [code] M. Gong, H. Zhang, H. Xu, X. Tian and J. Ma, "Multipatch Progressive Pansharpening With Knowledge Distillation," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-15, 2023, Art no. 5401115, doi: 10.1109/TGRS.2023.3254053.

  

• [TGRS 2023 S2DBPN] [paper] [code] K. Zhang, A. Wang, F. Zhang, W. Wan, J. Sun and L. Bruzzone, "Spatial-Spectral Dual Back-Projection Network for Pansharpening," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-16, 2023, Art no. 5402216, doi: 10.1109/TGRS.2023.3266799.

  

• [TGRS 2023 CADUI] [paper] [code] Z. Li, J. Li, F. Zhang and L. Fan, "CADUI: Cross-Attention-Based Depth Unfolding Iteration Network for Pansharpening Remote Sensing Images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-20, 2023, Art no. 5402420, doi: 10.1109/TGRS.2023.3267841.

  

• [TGRS 2023 LNM-PS] [paper] [code] R. Wen, L. -J. Deng, Z. -C. Wu, X. Wu and G. Vivone, "A Novel Spatial Fidelity With Learnable Nonlinear Mapping for Panchromatic Sharpening," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-15, 2023, Art no. 5401915, doi: 10.1109/TGRS.2023.3265404.

  

• [TGRS 2023 UAPN] [paper] [code] K. Zheng, J. Huang, M. Zhou, D. Hong and F. Zhao, "Deep Adaptive Pansharpening via Uncertainty-Aware Image Fusion," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-15, 2023, Art no. 5403715, doi: 10.1109/TGRS.2023.3269139.

  

• [TGRS 2023 CFF] [paper] [code] C. Ke, W. Zhang, Z. Wang, J. Ma and X. Tian, "Coarse-to-fine Cross-domain Learning Fusion Network for Pansharpening," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3299336.

  

• [TGRS 2023 ABFNet] [paper] [code]X. Zhao, J. Guo, Y. Zhang and Y. Wu, "Asymmetric Bidirectional Fusion Network for Remote Sensing Pansharpening," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-16, 2023, Art no. 5404816, doi: 10.1109/TGRS.2023.3296510.

  

• [TGRS 2023 UTSN] [paper] [code] Z. Sheng, F. Zhang, J. Sun, Y. Tan, K. Zhang and L. Bruzzone, "A Unified Two-Stage Spatial and Spectral Network With Few-Shot Learning for Pansharpening," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-17, 2023, Art no. 5403517, doi: 10.1109/TGRS.2023.3281602.

  

• [TGRS 2023 NLUNet] [paper] [code] X. Li, Y. Li, G. Shi, L. Zhang, W. Li and D. Lei, "Pansharpening Method Based on Deep Nonlocal Unfolding," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-11, 2023, Art no. 5404111, doi: 10.1109/TGRS.2023.3287532.

  

• [NeurIPS 2022 ARFNet] [paper] [code] K. Yan et al., “Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network,” in Advances in Neural Information Processing Systems, 2022, vol. 35, pp. 21988–22002. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2022/file/89ef9ce35c7833cba14bb2381ead6c54-Paper-Conference.pdf

• [ICCV 2023 PanFlowNet] [paper] [code] Gang Yang, Xaingyong Cao, Wenzhe Xiao, Man Zhou, Aiping Liu, Xun Chen, Deyu Meng. PanFlowNet: A Flow-Based Deep Network for Pan-sharpening[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision(ICCV). 2023.

  

Unsupervised

• [TGRS 2022 Z-PNN] [paper] [code] 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.

  

• [TGRS 2022 UCGAN] [paper] [code] H. Zhou, Q. Liu, D. Weng and Y. Wang, "Unsupervised Cycle-Consistent Generative Adversarial Networks for Pan Sharpening," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14, 2022, Art no. 5408814, doi: 10.1109/TGRS.2022.3166528.

  

• [ArXiv 2023 PLRDiff] [paper] [code] Rui, X., Cao, X., Zhu, Z., Yue, Z., & Meng, D. (2023). Unsupervised Pansharpening via Low-rank Diffusion Model. ArXiv, abs/2305.10925.

  

• [INFORM FUSION 2020 Pan-GAN] [paper] [code] 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, 2020, doi: https://doi.org/10.1016/j.inffus.2020.04.006.

  

• [J-STARS 2020] [paper] [code] S. Luo, S. Zhou, Y. Feng and J. Xie, "Pansharpening via Unsupervised Convolutional Neural Networks," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 4295-4310, 2020, doi: 10.1109/JSTARS.2020.3008047.

  

• [GRSL 2022 MetaPan] [paper] [code] D. Wang, P. Zhang, Y. Bai and Y. Li, "MetaPan: Unsupervised Adaptation With Meta-Learning for Multispectral Pansharpening," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 5513505, doi: 10.1109/LGRS.2022.3198141.

  

• [TGRS 2020 UP-SAM] [paper] [code] Y. Qu, R. K. Baghbaderani, H. Qi and C. Kwan, "Unsupervised Pansharpening Based on Self-Attention Mechanism," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 3192-3208, April 2021, doi: 10.1109/TGRS.2020.3009207.

  

• [CVPR 2020 UAL] [paper] [code] L. Zhang, J. Nie, W. Wei, Y. Zhang, S. Liao and L. Shao, "Unsupervised Adaptation Learning for Hyperspectral Imagery Super-Resolution," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 3070-3079, doi: 10.1109/CVPR42600.2020.00314.

  

• [TGRS 2022 SURE] [paper] [code] LH. V. Nguyen, M. O. Ulfarsson, J. R. Sveinsson and M. Dalla Mura, "Deep SURE for Unsupervised Remote Sensing Image Fusion," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13, 2022, Art no. 5412613, doi: 10.1109/TGRS.2022.3215902.

  

• [J-STARS 2022 LDP-Net] [paper] [code] J. Ni et al., "LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 5468-5479, 2022, doi: 10.1109/JSTARS.2022.3188181.

  

• [J-STARS 2021 PGMAN] [paper] [code] H. Zhou, Q. Liu and Y. Wang, "PGMAN: An Unsupervised Generative Multiadversarial Network for Pansharpening," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 6316-6327, 2021, doi: 10.1109/JSTARS.2021.3090252.

• [TGRS 2023 Lambda-PNN] [paper] [code] M. Ciotola, G. Poggi and G. Scarpa, "Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3299356.

  

• [TGRS 2023 Mun-GAN] [paper] [code] X. Liu, X. Liu, H. Dai, X. Kang, A. Plaza and W. Zu, "Mun-GAN: A Multiscale Unsupervised Network for Remote Sensing Image Pansharpening," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-18, 2023, Art no. 5404018, doi: 10.1109/TGRS.2023.3288073.