U-shaped pyramid dense residual network for pansharpening

References

• Aiazzi, B., L. Alparone, S. Baronti, A. Garzelli, and M. Selva. 2006. “MTF-Tailored Multiscale Fusion of High-Resolution MS and Pan Imagery.” Photogrammetric Engineering & Remote Sensing 72 (5): 591–596. https://doi.org/10.14358/PERS.72.5.591.
   Web of Science ®Google Scholar
• Alparone, L., B. Aiazzi, S. Baronti, A. Garzelli, F. Nencini, and M. Selva. 2008. “Multispectral and Panchromatic Data Fusion Assessment without Reference.” Photogrammetric Engineering & Remote Sensing 74 (2): 193–200. https://doi.org/10.14358/PERS.74.2.193.
   Web of Science ®Google Scholar
• Azarang, A., and H. Ghassemian. 2017. “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, 1–6.
   Google Scholar
• Burt, P., and E. Adelson. 1983. “The Laplacian Pyramid as a Compact Image Code.” IEEE Transactions on Communications 31 (4): 532–540. https://doi.org/10.1109/TCOM.1983.1095851.
   Web of Science ®Google Scholar
• Cai, J., and B. Huang. 2021. “Super-Resolution-Guided Progressive Pansharpening Based on a Deep Convolutional Neural Network.” IEEE Transactions on Geoscience and Remote Sensing 59 (6): 5206–5220. https://doi.org/10.1109/TGRS.2020.3015878.
   Web of Science ®Google Scholar
• Choi, J., Y. Kiyun, and Y. Kim. 2011. “A New Adaptive Component-Substitution-Based Satellite Image Fusion by Using Partial Replacement.” IEEE Transactions on Geoscience and Remote Sensing 49 (1): 295–309. https://doi.org/10.1109/TGRS.2010.2051674.
   Web of Science ®Google Scholar
• Dong, C., C. Change Loy, H. Kaiming, and X. Tang. 2016. “Image Super-Resolution Using Deep Convolutional Networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence 38 (2): 295–307. https://doi.org/10.1109/TPAMI.2015.2439281.
   PubMed Web of Science ®Google Scholar
• Du, Q., O. Gungor, and J. Shan. 2005. “Performance Evaluation for Pan-Sharpening Techniques.” In Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS ’05, Seoul Korea (South), Vol. 6, 4264–4266.
   Google Scholar
• Gao, H., Z. Liu, L. Van Der Maaten, and K. Q. Weinberger. 2017. “Densely Connected Convolutional Networks.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, 2261–2269.
   Google Scholar
• Garzelli, A., and F. Nencini. 2009. “Hypercomplex Quality Assessment of Multi/Hyperspectral Images.” IEEE Geoscience and Remote Sensing Letters 6 (4): 662–665. https://doi.org/10.1109/LGRS.2009.2022650.
   Web of Science ®Google Scholar
• Garzelli, A., F. Nencini, and L. Capobianco. 2008. “Optimal MMSE Pan Sharpening of Very High Resolution Multispectral Images.” IEEE Transactions on Geoscience and Remote Sensing 46 (1): 228–236. https://doi.org/10.1109/TGRS.2007.907604.
   Web of Science ®Google Scholar
• Ghassemian, H. 2016. “A Review of Remote Sensing Image Fusion Methods.” Information Fusion 32:75–89. https://doi.org/10.1016/j.inffus.2016.03.003.
   Web of Science ®Google Scholar
• Gillespie, A. R., A. B. Kahle, and R. E. Walker. 1987. “Color Enhancement of Highly Correlated Images. II. Channel Ratio and Chromaticity Transformation Techniques.” Remote Sensing of Environment 22 (3): 343–365. https://doi.org/10.1016/0034-4257(87)90088-5.
   Web of Science ®Google Scholar
• Hang, R., Q. Liu, and L. Zhu. 2021. “Spectral Super-Resolution Network Guided by Intrinsic Properties of Hyperspectral Imagery.” IEEE Transactions on Image Processing 30:7256–7265. https://doi.org/10.1109/TIP.2021.3104177.
   PubMed Web of Science ®Google Scholar
• Jie, Z., D. L. Civco, and J. A. Silander. 1998. “A Wavelet Transform Method to Merge Landsat TM and SPOT Panchromatic Data.” International Journal of Remote Sensing 19 (4): 743–757. https://doi.org/10.1080/014311698215973.
   Web of Science ®Google Scholar
• Jin, Z.-R., Y.-W. Zhuo, T.-J. Zhang, X.-X. Jin, S. Jing, and L.-J. Deng. 2022. “Remote Sensing Pansharpening by Full-Depth Feature Fusion.” Remote Sensing 14 (3): 3. https://www.mdpi.com/2072-4292/14/3/466.
   Google Scholar
• Kaiming, H., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, Nevada, 770–778.
   Google Scholar
• Kim, Y., C. Lee, D. Han, Y. Kim, and Y. Kim. 2011. “Improved Additive-Wavelet Image Fusion.” IEEE Geoscience and Remote Sensing Letters 8 (2): 263–267. https://doi.org/10.1109/LGRS.2010.2067192.
   Web of Science ®Google Scholar
• Laben, C. A., and B. V. Brower. 2000. “Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening.” Jan. 4. US Patent 6,011,875.
   Google Scholar
• Lei, D., Y. Huang, L. Zhang, and L. Weisheng. 2022. “Multibranch Feature Extraction and Feature Multiplexing Network for Pansharpening.” IEEE Transactions on Geoscience and Remote Sensing 60:1–13. https://doi.org/10.1109/TGRS.2021.3074624.
   Google Scholar
• Lin, T.-Y., P. Dollr, R. Girshick, H. Kaiming, B. Hariharan, and S. Belongie. 2017. “Feature Pyramid Networks for Object Detection.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, 936–944.
   Google Scholar
• Liu, X., Q. Liu, and Y. Wang. 2020. “Remote Sensing Image Fusion Based on Two-Stream Fusion Network.” Information Fusion 55:1–15. https://doi.org/10.1016/j.inffus.2019.07.010.
   Web of Science ®Google Scholar
• Liu, X., Y. Wang, and Q. Liu. 2018. “Psgan: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening.” In 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 873–877.
   Google Scholar
• Mallat, S. G. 1989. “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (7): 674–693. https://doi.org/10.1109/34.192463.
   Web of Science ®Google Scholar
• Masi, G., D. Cozzolino, L. Verdoliva, and G. Scarpa. 2016. “Pansharpening by Convolutional Neural Networks.” Remote Sensing 8 (7): 594. https://doi.org/10.3390/rs8070594.
   Web of Science ®Google Scholar
• Nunez, J., X. Otazu, O. Fors, A. Prades, V. Pala, and R. Arbiol. 1999. “Multiresolution-Based Image Fusion with Additive Wavelet Decomposition.” IEEE Transactions on Geoscience and Remote Sensing 37 (3): 1204–1211. https://doi.org/10.1109/36.763274.
   Web of Science ®Google Scholar
• Palubinskas, G. 2014. “Quality Assessment of Pan-Sharpening Methods.” In 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, 2526–2529.
   Google Scholar
• Scarpa, G., S. Vitale, and D. Cozzolino. 2018. “Target-Adaptive CNN-Based Pansharpening.” IEEE Transactions on Geoscience and Remote Sensing 56 (9): 5443–5457. https://doi.org/10.1109/TGRS.2018.2817393.
   Web of Science ®Google Scholar
• Shah, V. P., N. H. Younan, and R. 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): 1323–1335. https://doi.org/10.1109/TGRS.2008.916211.
   Web of Science ®Google Scholar
• Shutao, L., Q. Hao, X. Kang, and J. Atli Benediktsson. 2018. “Gaussian Pyramid Based Multiscale Feature Fusion for Hyperspectral Image Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (9): 3312–3324. https://doi.org/10.1109/JSTARS.2018.2856741.
   Web of Science ®Google Scholar
• Te-Ming, T., S. Shun-Chi, H.-C. Shyu, and P. S. Huang. 2001. “A New Look at IHS-Like Image Fusion Methods.” Information Fusion 2 (3): 177–186. https://doi.org/10.1016/S1566-2535(01)00036-7.
   Google Scholar
• Vivone, G., L. Alparone, J. Chanussot, M. Dalla Mura, A. Garzelli, G. A. Licciardi, R. Restaino, and L. Wald. 2015. “A Critical Comparison Among Pansharpening Algorithms.” IEEE Transactions on Geoscience and Remote Sensing 53 (5): 2565–2586. https://doi.org/10.1109/TGRS.2014.2361734.
   Web of Science ®Google Scholar
• Wald, L. 2000. “Quality of High Resolution Synthesised Images: Is There a Simple Criterion?” In Third conference“Fusion of Earth data: merging point measurements, raster maps and remotely sensed images”, 99–103. SEE/URISCA.
   Google Scholar
• Wald, L., T. Ranchin, and M. Mangolini. 1997. “Fusion of Satellite Images of Different Spatial Resolutions: Assessing the Quality of Resulting Images.” Photogrammetric Engineering and Remote Sensing 63 (6): 691–699.
   Web of Science ®Google Scholar
• Wang, Z., and A. C. Bovik. 2002. “A Universal Image Quality Index.” IEEE Signal Processing Letters 9 (3): 81–84. https://doi.org/10.1109/97.995823.
   Web of Science ®Google Scholar
• Wei, Y., and Q. Yuan. 2017. “Deep Residual Learning for Remote Sensed Imagery Pansharpening.” In 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China, 1–4.
   Google Scholar
• Xie, Y., W. Wei, H. Yang, W. Ning, and Y.-Q. Shen. 2021. “Detail Information Prior Net for Remote Sensing Image Pansharpening.” Remote Sensing 13 (14): 2800. https://doi.org/10.3390/rs13142800.
   Web of Science ®Google Scholar
• Xing, Y., M. Wang, S. Yang, and K. Zhang. 2018. “Pansharpening with Multiscale Geometric Support Tensor Machine.” IEEE Transactions on Geoscience and Remote Sensing 56 (5): 2503–2517. https://doi.org/10.1109/TGRS.2017.2742002.
   Web of Science ®Google Scholar
• Xiyan, H., L. Condat, J. M. Bioucas-Dias, J. Chanussot, and J. Xia. 2014. “A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors.” IEEE Transactions on Image Processing 23 (9): 4160–4174. https://doi.org/10.1109/TIP.2014.2333661.
   PubMed Web of Science ®Google Scholar
• Yang, Y., L. Hangyuan, S. Huang, and T. Wei. 2021. “Pansharpening Based on Joint-Guided Detail Extraction.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14:389–401. https://doi.org/10.1109/JSTARS.2020.3032472.
   Web of Science ®Google Scholar
• Yang, Y., T. Wei, S. Huang, L. Hangyuan, W. Wan, and L. Gan. 2022. “Dual-Stream Convolutional Neural Network with Residual Information Enhancement for Pansharpening.” IEEE Transactions on Geoscience and Remote Sensing 60:1–16. https://doi.org/10.1109/TGRS.2021.3098752.
   Google Scholar
• Yang, J., F. Xueyang, H. Yuwen, Y. Huang, X. Ding, and J. Paisley. 2017. “PanNet: A Deep Network Architecture for Pan-Sharpening.” In 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 1753–1761.
   Google Scholar
• Yuan, Q., Y. Wei, X. Meng, H. Shen, and L. Zhang. 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. https://doi.org/10.1109/JSTARS.2018.2794888.
   Web of Science ®Google Scholar
• Yuhas, R. H., A. F. Goetz, and J. W. Boardman. 1992. “Discrimination Among Semi-Arid Landscape Endmembers Using the Spectral Angle Mapper (SAM) Algorithm.” In JPL, Summaries of the Third Annual JPL Airborne Geoscience Workshop. Vol. 1, 147–149. AVIRIS Workshop.
   Google Scholar
• Zhou, F., R. Hang, Q. Liu, and X. Yuan. 2019. “Pyramid Fully Convolutional Network for Hyperspectral and Multispectral Image Fusion.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (5): 1549–1558. https://doi.org/10.1109/JSTARS.2019.2910990.
   Web of Science ®Google Scholar
• Zhu, X. X., and R. Bamler. 2013. “A Sparse Image Fusion Algorithm with Application to Pan-Sharpening.” IEEE Transactions on Geoscience and Remote Sensing 51 (5): 2827–2836. https://doi.org/10.1109/TGRS.2012.2213604.
   Web of Science ®Google Scholar