A task decoupled framework for enhancing the deep learning-based spatiotemporal fusion method

References

• Ao, Z., Y. Sun, X. Pan, and Q. Xin. 2022. “Deep Learning-Based Spatiotemporal Data Fusion Using a Patch-To-Pixel Mapping Strategy and Model Comparisons.” IEEE Transactions on Geoscience & Remote Sensing 60:1–18. https://doi.org/10.1109/TGRS.2022.3154406.
   Web of Science ®Google Scholar
• Cai, J., B. Huang, and T. Fung. 2022. “Progressive Spatiotemporal Image Fusion with Deep Neural Networks.” International Journal of Applied Earth Observation and Geoinformation 108 (October 2021): 102745. https://doi.org/10.1016/j.jag.2022.102745.
   Google Scholar
• Cao, H., X. Luo, Y. Peng, and T. Xie. 2022. “MANet: A Network Architecture for Remote Sensing Spatiotemporal Fusion Based on Multiscale and Attention Mechanisms.” Remote Sensing 14 (18): 4600. https://doi.org/10.3390/rs14184600.
   Web of Science ®Google Scholar
• Cheng, F., Z. Fu, B. Tang, L. Huang, K. Huang, and X. Ji. 2022. “STF-EGFA: A Remote Sensing Spatiotemporal Fusion Network with Edge-Guided Feature Attention.” Remote Sensing 14 (13): 3057. https://doi.org/10.3390/rs14133057.
   Web of Science ®Google Scholar
• Chen, G., P. Jiao, Q. Hu, L. Xiao, and Z. Ye. 2022. “SwinSTFM: Remote Sensing Spatiotemporal Fusion Using Swin Transformer.” IEEE Transactions on Geoscience & Remote Sensing 60:1–18. https://doi.org/10.1109/tgrs.2022.3182809.
   Web of Science ®Google Scholar
• Chen, J., L. Wang, R. Feng, P. Liu, W. Han, and X. Chen. 2021. “CycleGAN-STF: Spatiotemporal Fusion via CycleGAN-Based Image Generation.” IEEE Transactions on Geoscience & Remote Sensing 59 (7): 5851–5865. https://doi.org/10.1109/TGRS.2020.3023432. IEEE:.
   Web of Science ®Google Scholar
• Erdem, F., and U. Avdan. 2023. “STFRDN: A Residual Dense Network for Remote Sensing Image Spatiotemporal Fusion.” International Journal of Remote Sensing 44 (10): 3259–3277. https://doi.org/10.1080/01431161.2023.2221800.
   Web of Science ®Google Scholar
• Fang, S., Q. Guo, and Y. Cao. 2022. “WDBSTF: A Weighted Dual-Branch Spatiotemporal Fusion Network Based on Complementarity Between Super-Resolution and Change Prediction.” Remote Sensing 14 (22): 1–20. https://doi.org/10.3390/rs14225883.
   Web of Science ®Google Scholar
• Gao, F., M. Anderson, C. Daughtry, and D. Johnson. 2018. “Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery.” Remote Sensing 10 (9): 1489. https://doi.org/10.3390/rs10091489.
   Web of Science ®Google Scholar
• Gao, F., J. Masek, M. Schwaller, and F. Hall. 2006. “On the Blending of the Landsat and MODIS Surface Reflectance: Predicting Daily Landsat Surface Reflectance.” IEEE Transactions on Geoscience and Remote Sensing 44 (8): 2207–2218. https://doi.org/10.1109/TGRS.2006.872081.
   Web of Science ®Google Scholar
• Gao, H., X. Zhu, Q. Guan, X. Yang, Y. Yao, W. Zeng, and X. Peng. 2021. “CuFSDAF: An Enhanced Flexible Spatiotemporal Data Fusion Algorithm Parallelized Using Graphics Processing Units.” IEEE Transactions on Geoscience & Remote Sensing 60:1–16. https://doi.org/10.1109/TGRS.2021.3080384.
   Web of Science ®Google Scholar
• Guo, D., W. Shi, F. Qian, S. Wang, and C. Cai. 2022a. “Monitoring the Spatiotemporal Change of Dongting Lake Wetland by Integrating Landsat and MODIS Images, from 2001 to 2020.” Ecological Informatics 72:101848. https://doi.org/10.1016/j.ecoinf.2022.101848.
   Web of Science ®Google Scholar
• Guo, D., W. Shi, H. Zhang, and M. Hao. 2022b. “A Flexible Object-Level Processing Strategy to Enhance the Weight Function-Based Spatiotemporal Fusion Method.” IEEE Transactions on Geoscience and Remote Sensing 60. https://doi.org/10.1109/TGRS.2022.3212474.
   Google Scholar
• Guo, D., S. Wenzhong, H. Ming, and Z. Xiaolin. 2020. “FSDAF 2.0: Improving the Performance of Retrieving Land Cover Changes and Preserving Spatial Details.” Remote Sensing of Environment 248:111973. https://doi.org/10.1016/j.rse.2020.111973.
   Web of Science ®Google Scholar
• Hou, S., W. Sun, B. Guo, L. Xiaodong, J. Zhang, X. Changzhi, L. Xiaobo, Y. Shao, and L. Cheng 2022. “RFSDAF: A New Spatiotemporal Fusion Method Robust to Registration Errors.” IEEE Transactions on Geoscience and Remote Sensing 60. https://doi.org/10.1109/TGRS.2021.3138078.
   Google Scholar
• Huang, B., and H. Song. 2012. “Spatiotemporal Reflectance Fusion via Sparse Representation.” IEEE Transactions on Geoscience & Remote Sensing 50 (10): 3707–3716. https://doi.org/10.1109/TGRS.2012.2186638.
   Web of Science ®Google Scholar
• Huang, B., J. Wang, H. Song, F. Dongjie, and K. Wong. 2013. “Generating High Spatiotemporal Resolution Land Surface Temperature for Urban Heat Island Monitoring.” IEEE Geoscience and Remote Sensing Letters 10 (5): 1011–1015. https://doi.org/10.1109/lgrs.2012.2227930.
   Google Scholar
• Huang, B., and H. Zhang. 2014. “Spatio-Temporal Reflectance Fusion via Unmixing: Accounting for Both Phenological and Land-Cover Changes.” International Journal of Remote Sensing 35 (16): 6213–6233. https://doi.org/10.1080/01431161.2014.951097.
   Web of Science ®Google Scholar
• Jia, D., C. Cheng, S. Shen, and L. Ning. 2022. “Multitask Deep Learning Framework for Spatiotemporal Fusion of NDVI.” IEEE Transactions on Geoscience and Remote Sensing 60. https://doi.org/10.1109/TGRS.2021.3140144.
   Google Scholar
• Jia, D., C. Cheng, C. Song, S. Shen, L. Ning, and T. Zhang. 2021. “A Hybrid Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions.” Remote Sensing 13 (4): 645. https://doi.org/10.3390/rs13040645.
   Web of Science ®Google Scholar
• Kong, F., M. Li, S. Liu, D. Liu, J. He, Y. Bai, F. Chen, and L. Fu. 2022. Residual Local Feature Network for Efficient Super-Resolution. Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit, New Orleans, LA, USA. 766–776. https://doi.org/10.1109/cvprw56347.2022.00092.
   Google Scholar
• Lei, D., M. Bai, L. Zhang, and L. Weisheng. 2022. “Convolution Neural Network with Edge Structure Loss for Spatiotemporal Remote Sensing Image Fusion.” International Journal of Remote Sensing 43 (3): 1015–1036. https://doi.org/10.1080/01431161.2022.2030070.
   Web of Science ®Google Scholar
• Liao, C., J. Wang, T. Dong, J. Shang, J. Liu, and Y. Song. 2019. “Using Spatio-Temporal Fusion of Landsat-8 and MODIS Data to Derive Phenology, Biomass and Yield Estimates for Corn and Soybean.” The Science of the Total Environment 650:1707–1721. https://doi.org/10.1016/j.scitotenv.2018.09.308.
   PubMed Web of Science ®Google Scholar
• Li, Y., R. Cai, L. Jun, Z. Liu, L. Meng, and H. Lin. 2022. “Pansharpening-Based Spatio-Temporal Fusion for Predicting Intense Surface Changes.” IEEE Transactions on Geoscience & Remote Sensing 60:1–14. https://doi.org/10.1109/TGRS.2022.3169494.
   Web of Science ®Google Scholar
• Li, X., G. M. Foody, D. S. Boyd, Y. Ge, Y. Zhang, Y. Du, and F. Ling. 2020. “SFSDAF: An Enhanced FSDAF That Incorporates Sub-Pixel Class Fraction Change Information for Spatio-Temporal Image Fusion.” Remote Sensing of Environment 237:111537. https://doi.org/10.1016/j.rse.2019.111537.
   Web of Science ®Google Scholar
• Liu, M., X. Liu, X. Dong, B. Zhao, X. Zou, W. Ling, and H. Wei. 2020. “An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on Estarfm.” Remote Sensing 12 (21): 3673. https://doi.org/10.3390/rs12213673.
   Web of Science ®Google Scholar
• Liu, J., W. Zhang, Y. Tang, T. Jie, and W. Gangshan 2020. “Residual Feature Aggregation Network for Image Super-Resolution.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1:2356–2365. https://doi.org/10.1109/CVPR42600.2020.00243.
   Google Scholar
• Li, W., C. Yang, Y. Peng, and D. Jiao. 2022. “A Pseudo-Siamese Deep Convolutional Neural Network for Spatiotemporal Satellite Image Fusion.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 15:1205–1220. https://doi.org/10.1109/JSTARS.2022.3143464.
   Web of Science ®Google Scholar
• Ma, J., W. Zhang, A. Marinoni, and L. Gao. 2018. “An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images.” Remote Sensing 10 (9): 1388. https://doi.org/10.3390/rs10091388.
   Web of Science ®Google Scholar
• Peng, K., Q. Wang, Y. Tang, X. Tong, S. Member, and P. M. Atkinson. 2021. “Geographically Weighted Spatial Unmixing for Spatiotemporal Fusion.” IEEE Transactions on Geoscience & Remote Sensing 60:1–17. https://doi.org/10.1109/TGRS.2021.3115136.
   Web of Science ®Google Scholar
• Qin, P., H. Huang, H. Tang, J. Wang, and C. Liu. 2022. “MUSTFN: A Spatiotemporal Fusion Method for Multi-Scale and Multi-Sensor Remote Sensing Images Based on a Convolutional Neural Network.” International Journal of Applied Earth Observation and Geoinformation 115 (September): 103113. https://doi.org/10.1016/j.jag.2022.103113. Elsevier B.V.: 103113.
   Web of Science ®Google Scholar
• Quan, J., W. Zhan, T. Ma, Y. Du, Z. Guo, and B. Qin. 2018. “An Integrated Model for Generating Hourly Landsat-Like Land Surface Temperatures Over Heterogeneous Landscapes.” Remote Sensing of Environment 206:403–423. https://doi.org/10.1016/j.rse.2017.12.003.
   Web of Science ®Google Scholar
• Shen, H., W. Penghai, Y. Liu, A. Tinghua, and Y. Wang. 2013. “A Spatial and Temporal Reflectance Fusion Model Considering Sensor Observation Differences.” International Journal of Remote Sensing 34 (12): 4367–4383. https://doi.org/10.1080/01431161.2013.777488.
   Web of Science ®Google Scholar
• Shi, W., D. Guo, and H. Zhang. 2022. “A Reliable and Adaptive Spatiotemporal Data Fusion Method for Blending Multi-Spatiotemporal-Resolution Satellite Images.” Remote Sensing of Environment 268:112770. https://doi.org/10.1016/j.rse.2021.112770.
   Web of Science ®Google Scholar
• Shi, W., Y. Yue, Z. Liu, R. Chen, and L. Chen. 2022. “A Deep-Learning Approach for Modelling Pedestrian Movement Uncertainty in Large- Scale Indoor Areas.” International Journal of Applied Earth Observation and Geoinformation 114 (June): 103065. https://doi.org/10.1016/j.jag.2022.103065.
   Web of Science ®Google Scholar
• Song, H., Q. Liu, G. Wang, R. Hang, and B. Huang. 2018. “Spatiotemporal Satellite Image Fusion Using Deep Convolutional Neural Networks.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 11 (3): 821–829. https://doi.org/10.1109/JSTARS.2018.2797894.
   Web of Science ®Google Scholar
• Sun, Y., H. Zhang, and W. Shi. 2019. “A Spatio-Temporal Fusion Method for Remote Sensing Data Using a Linear Injection Model and Local Neighbourhood Information.” International Journal of Remote Sensing 40 (8): 2965–2985. https://doi.org/10.1080/01431161.2018.1538585.
   Web of Science ®Google Scholar
• Tan, Z., L. Di, M. Zhang, L. Guo, and M. Gao. 2019. “An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion.” Remote Sensing 11 (24): 2898. https://doi.org/10.3390/rs11242898.
   Web of Science ®Google Scholar
• Tan, Z., M. Gao, L. Xinghua, and L. Jiang. 2021. “A Flexible Reference-Insensitive Spatiotemporal Fusion Model for Remote Sensing Images Using Conditional Generative Adversarial Network.” IEEE Transactions on Geoscience & Remote Sensing 60:1–13. https://doi.org/10.1109/TGRS.2021.3050551.
   Web of Science ®Google Scholar
• Tan, Z., M. Gao, J. Yuan, L. Jiang, and H. Duan. 2022. “A Robust Model for MODIS and Landsat Image Fusion Considering Input Noise.” IEEE Transactions on Geoscience and Remote Sensing 60. https://doi.org/10.1109/TGRS.2022.3145086.
   Google Scholar
• Tang, Y., Q. Wang, and P. M. Atkinson. 2023. “Filling Then Spatio-Temporal Fusion for All-Sky MODIS Land Surface Temperature Generation.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16. 1350–1364. https://doi.org/10.1109/JSTARS.2023.3235940.
   Google Scholar
• Tan, Z., P. Yue, L. Di, and J. Tang. 2018. “Deriving High Spatiotemporal Remote Sensing Images Using Deep Convolutional Network.” Remote Sensing 10 (7): 1066. https://doi.org/10.3390/rs10071066.
   Web of Science ®Google Scholar
• Wang, Q., and P. M. Atkinson. 2018. “Spatio-Temporal Fusion for Daily Sentinel-2 Images.” Remote Sensing of Environment 204:31–42. https://doi.org/10.1016/j.rse.2017.10.046.
   Web of Science ®Google Scholar
• Wang, P., B. Bayram, and E. Sertel. 2022. “A Comprehensive Review on Deep Learning Based Remote Sensing Image Super-Resolution Methods.” Earth-Science Reviews 232 (January): 104110.https://doi.org/10.1016/j.earscirev.2022.104110.
   Web of Science ®Google Scholar
• Wang, Q., X. Ding, X. Tong, and P. M. Atkinson. 2022. “Real-Time Spatiotemporal Spectral Unmixing of MODIS Images.” IEEE Transactions on Geoscience & Remote Sensing 60:1–16. https://doi.org/10.1109/TGRS.2021.3108540.
   Web of Science ®Google Scholar
• Wang, Z., and Z. Gao. 2022. “Dynamic Monitoring of Flood Disaster Based on Remote Sensing Data Cube.” Natural Hazards, 114:31233138. , 114:31233138. https://doi.org/10.1007/s11069-022-05508-3.
   Google Scholar
• Wang, Q., K. Peng, Y. Tang, X. Tong, and P. M. Atkinson. 2021. “Blocks-Removed Spatial Unmixing for Downscaling MODIS Images.” Remote Sensing of Environment 256:112325. https://doi.org/10.1016/j.rse.2021.112325.
   Web of Science ®Google Scholar
• Wang, L., M. Zhang, X. Shen, and W. Shi. 2023. “Landslide Mapping Using Multi-Level Feature Enhancement Change Detection Network.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 16:3599–3610. https://doi.org/10.1109/JSTARS.2023.3245062.
   Web of Science ®Google Scholar
• Weng, Q., F. Peng, and F. Gao. 2014. “Generating Daily Land Surface Temperature at Landsat Resolution by Fusing Landsat and MODIS Data.” Remote Sensing of Environment 145:55–67. https://doi.org/10.1016/j.rse.2014.02.003.
   Web of Science ®Google Scholar
• Xu, C., X. Du, Z. Yan, J. Zhu, S. Xu, and X. Fan. 2022. “VSDF: A Variation-Based Spatiotemporal Data Fusion Method.” Remote Sensing of Environment 283 (October): 113309. https://doi.org/10.1016/j.rse.2022.113309. Elsevier Inc.
   Web of Science ®Google Scholar
• Yang, G., Y. Qian, H. Liu, B. Tang, Q. Ranran, Y. Lu, and J. Geng. 2022. “MSFusion: Multistage for Remote Sensing Image Spatiotemporal Fusion Based on Texture Transformer and Convolutional Neural Network.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 15:4653–4666. https://doi.org/10.1109/jstars.2022.3179415.
   Web of Science ®Google Scholar
• Zhang, H., Y. Sun, W. Shi, D. Guo, and N. Zheng. 2021. “An Object-Based Spatiotemporal Fusion Model for Remote Sensing Images.” European Journal of Remote Sensing 54 (1): 86–101. https://doi.org/10.1080/22797254.2021.1879683.
   Web of Science ®Google Scholar
• Zhang, M., Y. Zeng, W. Huang, and L. Songnian. 2019. “Combining Spatiotemporal Fusion and Object-Based Image Analysis for Improving Wetland Mapping in Complex and Heterogeneous Urban Landscapes.” Geocarto International 34 (10): 1144–1161. https://doi.org/10.1080/10106049.2018.1474275.
   Web of Science ®Google Scholar
• Zhou, Y., T. Liu, O. Batelaan, L. Duan, Y. Wang, L. Xia, and L. Mingyang 2023. “Spatiotemporal Fusion of Multi-Source Remote Sensing Data for Estimating Aboveground Biomass of Grassland.” Ecological Indicators 146 (April 2022): 109892. https://doi.org/10.1016/j.ecolind.2023.109892.
   Google Scholar
• Zhu, X., F. Cai, J. Tian, and T. Kay Ann Williams. 2018. “Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions.” Remote Sensing 10 (4): 527. https://doi.org/10.3390/rs10040527.
   Web of Science ®Google Scholar
• Zhu, X., J. Chen, F. Gao, X. Chen, and J. G. Masek. 2010. “An Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model for Complex Heterogeneous Regions.” Remote Sensing of Environment 114 (11): 2610–2623. https://doi.org/10.1016/j.rse.2010.05.032.
   Web of Science ®Google Scholar
• Zhu, X., E. H. Helmer, F. Gao, D. Liu, J. Chen, and M. A. Lefsky. 2016. “A Flexible Spatiotemporal Method for Fusing Satellite Images with Different Resolutions.” Remote Sensing of Environment 172:165–177. https://doi.org/10.1016/j.rse.2015.11.016.
   Web of Science ®Google Scholar
• Zhukov, B., D. Oertel, F. Lanzl, and G. Reinhäckel. 1999. “Unmixing-Based Multisensor Multiresolution Image Fusion.” IEEE Transactions on Geoscience & Remote Sensing 37 (3): 1212–1226. https://doi.org/10.1109/36.763276.
   Web of Science ®Google Scholar
• Zhu, Z., X. Mengdu, S. Bai, T. Huang, and X. Bai. 2019. “Asymmetric Non-Local Neural Networks for Semantic Segmentation.” Proceedings of the IEEE International Conference on Computer Vision 2019-Oct(Iccv): 593–602. https://doi.org/10.1109/ICCV.2019.00068.
   Google Scholar
• Zhu, Z., Y. Tao, and X. Luo. 2022. “Hybrid Deep-Learning Network for Rapid On-Site Peak Ground Velocity Prediction.” IEEE Transactions on Geoscience & Remote Sensing 60:1–12. https://doi.org/10.1109/tgrs.2022.3177749.
   Web of Science ®Google Scholar
• Zhu, X., W. Zhan, J. Zhou, X. Chen, Z. Liang, X. Shuai, and J. Chen. 2022. “A Novel Framework to Assess All-Round Performances of Spatiotemporal Fusion Models.” Remote Sensing of Environment 274:113002. https://doi.org/10.1016/j.rse.2022.113002.
   Web of Science ®Google Scholar
• Zurita-Milla, R., G. P. W. C. Jan, and M. E. Schaepman. 2008. “Unmixing-Based Landsat TM and MERIS FR Data Fusion.” IEEE Geoscience & Remote Sensing Letters 5 (3): 453–457. https://doi.org/10.1109/LGRS.2008.919685.
   Web of Science ®Google Scholar