Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
175 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Real HSI-MSI-PAN image dataset for the hyperspectral/multi-spectral/panchromatic image fusion and super-resolution fields (2407.02387v2)

Published 2 Jul 2024 in cs.CV and eess.IV

Abstract: Nowadays, most of the hyperspectral image (HSI) fusion experiments are based on simulated datasets to compare different fusion methods. However, most of the spectral response functions and spatial downsampling functions used to create the simulated datasets are not entirely accurate, resulting in deviations in spatial and spectral features between the generated images for fusion and the real images for fusion. This reduces the credibility of the fusion algorithm, causing unfairness in the comparison between different algorithms and hindering the development of the field of hyperspectral image fusion. Therefore, we release a real HSI/MSI/PAN image dataset to promote the development of the field of hyperspectral image fusion. These three images are spatially registered, meaning fusion can be performed between HSI and MSI, HSI and PAN image, MSI and PAN image, as well as among HSI, MSI, and PAN image. This real dataset could be available at https://aistudio.baidu.com/datasetdetail/281612. The related code to process the data could be available at https://github.com/rs-lsl/CSSNet.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. Q. Zhu, W. Deng, Z. Zheng, Y. Zhong, Q. Guan, W. Lin, L. Zhang, and D. Li, “A spectral-spatial-dependent global learning framework for insufficient and imbalanced hyperspectral image classification,” IEEE Transactions on Cybernetics, pp. 1–15, 2021.
  2. K. Zheng, L. Gao, W. Liao, D. Hong, B. Zhang, X. Cui, and J. Chanussot, “Coupled convolutional neural network with adaptive response function learning for unsupervised hyperspectral super resolution,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 3, pp. 2487–2502, 2021.
  3. N. Yokoya, J. C.-W. Chan, and K. Segl, “Potential of resolution-enhanced hyperspectral data for mineral mapping using simulated enmap and sentinel-2 images,” Remote Sensing, vol. 8, no. 3, 2016.
  4. V. Salomonson, W. Barnes, P. Maymon, H. Montgomery, and H. Ostrow, “Modis: advanced facility instrument for studies of the earth as a system,” IEEE Transactions on Geoscience and Remote Sensing, vol. 27, no. 2, pp. 145–153, 1989.
  5. J. Yu, D. Liang, B. Han, and H. Gao, “Study on ground object classification based on the hyperspectral fusion images of ZY-1(02D) satellite,” Journal of Applied Remote Sensing, vol. 15, no. 4, p. 042603, 2021.
  6. Q. Wei, N. Dobigeon, and J.-Y. Tourneret, “Bayesian fusion of multi-band images,” IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 6, pp. 1117–1127, 2015.
  7. M. Simões, J. Bioucas‐Dias, L. B. Almeida, and J. Chanussot, “A convex formulation for hyperspectral image superresolution via subspace-based regularization,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 6, pp. 3373–3388, 2015.
  8. Y. Zheng, J. Li, Y. Li, J. Guo, X. Wu, and J. Chanussot, “Hyperspectral pansharpening using deep prior and dual attention residual network,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 11, pp. 8059–8076, 2020.
  9. J. Qu, S. Hou, W. Dong, S. Xiao, Q. Du, and Y. Li, “A dual-branch detail extraction network for hyperspectral pansharpening,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–13, 2022.
  10. W. G. C. Bandara and V. M. Patel, “Hypertransformer: A textural and spectral feature fusion transformer for pansharpening,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022, pp. 1767–1777.
  11. S. Li, Y. Tian, H. Xia, and Q. Liu, “Unmixing-based pan-guided fusion network for hyperspectral imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–17, 2022.
  12. S. Li, Y. Tian, C. Wang, H. Wu, and S. Zheng, “Hyperspectral image super-resolution network based on cross-scale nonlocal attention,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–15, 2023.
  13. L. Loncan, L. B. de Almeida, J. M. Bioucas-Dias, X. Briottet, J. Chanussot, N. Dobigeon, S. Fabre, W. Liao, G. A. Licciardi, M. Simões, J.-Y. Tourneret, M. A. Veganzones, G. Vivone, Q. Wei, and N. Yokoya, “Hyperspectral pansharpening: A review,” IEEE Geoscience and Remote Sensing Magazine, vol. 3, no. 3, pp. 27–46, 2015.
  14. L. He, J. Zhu, J. Li, A. Plaza, J. Chanussot, and B. Li, “Hyperpnn: Hyperspectral pansharpening via spectrally predictive convolutional neural networks,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 8, pp. 3092–3100, 2019.
  15. L. He, J. Zhu, J. Li, D. Meng, J. Chanussot, and A. Plaza, “Spectral-fidelity convolutional neural networks for hyperspectral pansharpening,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 5898–5914, 2020.
  16. D. He and Y. Zhong, “Deep hierarchical pyramid network with high- frequency -aware differential architecture for super-resolution mapping,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–15, 2023.
  17. J. Qu, Z. Xu, W. Dong, S. Xiao, Y. Li, and Q. Du, “A spatio-spectral fusion method for hyperspectral images using residual hyper-dense network,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–15, 2022.
  18. W. G. C. Bandara, J. M. J. Valanarasu, and V. M. Patel, “Hyperspectral pansharpening based on improved deep image prior and residual reconstruction,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–16, 2022.
  19. K. Li, W. Xie, Q. Du, and Y. Li, “Ddlps: Detail-based deep laplacian pansharpening for hyperspectral imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 10, pp. 8011–8025, 2019.
  20. W. Dong, Y. Yang, J. Qu, W. Xie, and Y. Li, “Fusion of hyperspectral and panchromatic images using generative adversarial network and image segmentation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–13, 2022.
  21. P. Guan and E. Y. Lam, “Multistage dual-attention guided fusion network for hyperspectral pansharpening,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–14, 2022.
  22. S. Li, S. Li, and L. Zhang, “Hyperspectral and panchromatic images fusion based on the dual conditional diffusion models,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–15, 2023.
  23. R. Dian, S. Li, and X. Kang, “Regularizing hyperspectral and multispectral image fusion by cnn denoiser,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 3, pp. 1124–1135, 2021.
  24. S. Li, R. Dian, and H. Liu, “Learning the external and internal priors for multispectral and hyperspectral image fusion,” Science China Information Sciences, vol. 66, no. 4, p. 140303, 2023.
  25. R. Dian, A. Guo, and S. Li, “Zero-shot hyperspectral sharpening,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 10, pp. 12 650–12 666, 2023.
  26. N. Yokoya, T. Yairi, and A. Iwasaki, “Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 2, pp. 528–537, 2012.
  27. R. Dian, L. Fang, and S. Li, “Hyperspectral image super-resolution via non-local sparse tensor factorization,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.
  28. S. Li, R. Dian, L. Fang, and J. M. Bioucas-Dias, “Fusing hyperspectral and multispectral images via coupled sparse tensor factorization,” IEEE Transactions on Image Processing, vol. 27, no. 8, pp. 4118–4130, 2018.
  29. R. Dian, S. Li, A. Guo, and L. Fang, “Deep hyperspectral image sharpening,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 11, pp. 5345–5355, 2018.
  30. R. Dian, S. Li, L. Fang, T. Lu, and J. M. Bioucas-Dias, “Nonlocal sparse tensor factorization for semiblind hyperspectral and multispectral image fusion,” IEEE Transactions on Cybernetics, vol. 50, no. 10, pp. 4469–4480, 2020.
  31. R. Dian and S. Li, “Hyperspectral image super-resolution via subspace-based low tensor multi-rank regularization,” IEEE Transactions on Image Processing, vol. 28, no. 10, pp. 5135–5146, 2019.
  32. J. Xiao, J. Li, Q. Yuan, and L. Zhang, “A dual-unet with multistage details injection for hyperspectral image fusion,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–13, 2022.
  33. Y. Zheng, J. Li, Y. Li, J. Guo, X. Wu, Y. Shi, and J. Chanussot, “Edge-conditioned feature transform network for hyperspectral and multispectral image fusion,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–15, 2022.
  34. K. Zhang, M. Wang, S. Yang, and L. Jiao, “Spatial–spectral-graph-regularized low-rank tensor decomposition for multispectral and hyperspectral image fusion,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 4, pp. 1030–1040, 2018.
  35. C. I. Kanatsoulis, X. Fu, N. D. Sidiropoulos, and W.-K. Ma, “Hyperspectral super-resolution: A coupled tensor factorization approach,” IEEE Transactions on Signal Processing, vol. 66, no. 24, pp. 6503–6517, 2018.
  36. Q. Xie, M. Zhou, Q. Zhao, D. Meng, W. Zuo, and Z. Xu, “Multispectral and hyperspectral image fusion by ms/hs fusion net,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
  37. Z. Wang, B. Chen, R. Lu, H. Zhang, H. Liu, and P. K. Varshney, “Fusionnet: An unsupervised convolutional variational network for hyperspectral and multispectral image fusion,” IEEE Transactions on Image Processing, vol. 29, pp. 7565–7577, 2020.
  38. Y. Liu, X. Cheng, H. Li, S. Luo, and B. Yang, “All in one: A unified network for hyperspectral image fusion,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–16, 2024.
  39. K. Li, W. Zhang, D. Yu, and X. Tian, “Hypernet: A deep network for hyperspectral, multispectral, and panchromatic image fusion,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 188, pp. 30–44, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S092427162200096X
  40. X. Tian, W. Zhang, Y. Chen, Z. Wang, and J. Ma, “Hyperfusion: A computational approach for hyperspectral, multispectral, and panchromatic image fusion,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–16, 2022.
  41. R. Arablouei, “Fusing multiple multiband images,” Journal of Imaging, vol. 4, no. 10, 2018. [Online]. Available: https://www.mdpi.com/2313-433X/4/10/118
  42. X. Tian, K. Li, W. Zhang, Z. Wang, and J. Ma, “Interpretable model-driven deep network for hyperspectral, multispectral, and panchromatic image fusion,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–14, 2023.
  43. N. Yokoya, T. Yairi, and A. Iwasaki, “Hyperspectral, multispectral, and panchromatic data fusion based on coupled non-negative matrix factorization,” in 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011, pp. 1–4.
  44. 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.

Summary

We haven't generated a summary for this paper yet.

Github Logo Streamline Icon: https://streamlinehq.com

GitHub

X Twitter Logo Streamline Icon: https://streamlinehq.com