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In this paper, we propose the GAN spatiotemporal fusion model Based on multiscale and convolutional block attention module (CBAM) for remote sensing images (MCBAM-GAN) to produce high-quality HTHS fusion images. The model is divided into three stages: multi-level feature extraction, multi-feature fusion, and multi-scale reconstruction. First of all, we use the U-NET structure in the generator to deal with the significant differences in image resolution while avoiding the reduction in resolution due to the limitation of GPU memory. Second, a flexible CBAM module is added to adaptively re-scale the spatial and channel features without increasing the computational cost, to enhance the salient areas and extract more detailed features. Considering that features of different scales play an essential role in the fusion, the idea of multiscale is added to extract features of different scales in different scenes and finally use them in the multi loss reconstruction stage. Finally, to check the validity of MCBAM-GAN model, we test it on LGC and CIA datasets and compare it with the classical algorithm for spatiotemporal fusion. The results show that the model performs well in this paper.<\/jats:p>","DOI":"10.3390\/rs15061583","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T06:14:58Z","timestamp":1678774498000},"page":"1583","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["MCBAM-GAN: The Gan Spatiotemporal Fusion Model Based on Multiscale and CBAM for Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Hui","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830014, China"},{"name":"Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Urumqi 830011, China"},{"name":"Key Laboratory of Software Engineering, Xinjiang University, Urumqi 830008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangqi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University, Urumqi 830008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengliang","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University, Urumqi 830008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yurong","family":"Qian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Urumqi 830011, China"},{"name":"Key Laboratory of Software Engineering, Xinjiang University, Urumqi 830008, China"},{"name":"School of Software, Xinjiang University, Urumqi 830008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingying","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830014, China"},{"name":"Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Urumqi 830011, China"},{"name":"Key Laboratory of Software Engineering, Xinjiang University, Urumqi 830008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100901","DOI":"10.1117\/1.OE.60.10.100901","article-title":"Research on super-resolution reconstruction of remote sensing images: A comprehensive review","volume":"60","author":"Liu","year":"2021","journal-title":"Opt. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1080\/01431161.2020.1809742","article-title":"Spatiotemporal fusion of remote sensing images using a convolutional neural network with attention and multiscale mechanisms","volume":"42","author":"Li","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.rse.2011.10.014","article-title":"Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology","volume":"117","author":"Walker","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","article-title":"When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/TGRS.2020.2991407","article-title":"Automatic Weakly Supervised Object Detection from High Spatial Resolution Remote Sensing Images via Dynamic Curriculum Learning","volume":"59","author":"Yao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.1109\/TGRS.2020.3020823","article-title":"Multimodal GANs: Toward Crossmodal Hyperspectral-Multispectral Image Segmentation","volume":"59","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1016\/j.scitotenv.2017.09.103","article-title":"Potential for using remote sensing to estimate carbon fluxes across northern peatlands\u2014A review","volume":"615","author":"Lees","year":"2018","journal-title":"Sci. Total. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.rse.2007.05.017","article-title":"Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil","volume":"112","author":"Galford","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"929","DOI":"10.14358\/PERS.79.10.929","article-title":"Web-service-based monitoring and analysis of global agricultural drought","volume":"79","author":"Deng","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"140301","DOI":"10.1007\/s11432-019-2785-y","article-title":"Spatio-temporal fusion for remote sensing data: An overview and new benchmark","volume":"63","author":"Li","year":"2020","journal-title":"Sci. China Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1109\/36.701075","article-title":"The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research","volume":"36","author":"Justice","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Montreal, QC, Canada."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhu, J., Kr\u00e4henb\u00fchl, P., Shechtman, E., and Efros, A.A. (2016, January 11\u201314). Generative Visual Manipulation on the Natural Image Manifold. Proceedings of the Computer Vision\u2014ECCV 2016\u201414th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46454-1_36"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, G., Reda, F.A., Shih, K.J., Wang, T., Tao, A., and Catanzaro, B. (2018, January 8\u201314). Image Inpainting for Irregular Holes Using Partial Convolutions. Proceedings of the Computer Vision\u2014ECCV 2018\u201415th European Conference, Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_6"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A.P., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4273","DOI":"10.1109\/TGRS.2020.3010530","article-title":"Remote Sensing Image Spatiotemporal Fusion Using a Generative Adversarial Network","volume":"59","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5601413","DOI":"10.1109\/TGRS.2021.3050551","article-title":"A Flexible Reference-Insensitive Spatiotemporal Fusion Model for Remote Sensing Images Using Conditional Generative Adversarial Network","volume":"60","author":"Tan","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5851","DOI":"10.1109\/TGRS.2020.3023432","article-title":"CycleGAN-STF: Spatiotemporal Fusion via CycleGAN-Based Image Generation","volume":"59","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein Generative Adversarial Networks. Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1109\/36.763276","article-title":"Unmixing-based multisensor multiresolution image fusion","volume":"37","author":"Zhukov","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"063507","DOI":"10.1117\/1.JRS.6.063507","article-title":"Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model","volume":"6","author":"Wu","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5346","DOI":"10.3390\/rs5105346","article-title":"An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data","volume":"5","author":"Zhang","year":"2013","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.rse.2016.07.028","article-title":"Land cover change detection by integrating object-based data blending model of Landsat and MODIS","volume":"184","author":"Lu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1016\/j.rse.2009.03.007","article-title":"A new data fusion model for high spatial-and temporal-resolution mapping of forest disturbance based on Landsat and MODIS","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2014.02.003","article-title":"Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data","volume":"145","author":"Weng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.rse.2013.03.021","article-title":"Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method","volume":"135","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liao, L., Song, J., Wang, J., Xiao, Z., and Wang, J. (2016). Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI. Remote Sens., 8.","DOI":"10.3390\/rs8060452"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7135","DOI":"10.1109\/TGRS.2016.2596290","article-title":"An Integrated Framework for the Spatio-Temporal-Spectral Fusion of Remote Sensing Images","volume":"54","author":"Shen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xue, J., Leung, Y., and Fung, T. (2017). A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images. Remote Sens., 9.","DOI":"10.3390\/rs9121310"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1109\/TGRS.2012.2186638","article-title":"Spatiotemporal Reflectance Fusion via Sparse Representation","volume":"50","author":"Huang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.1109\/TGRS.2012.2213095","article-title":"Spatiotemporal Satellite Image Fusion Through One-Pair Image Learning","volume":"51","author":"Song","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6791","DOI":"10.1109\/TGRS.2015.2448100","article-title":"An Error-Bound-Regularized Sparse Coding for Spatiotemporal Reflectance Fusion","volume":"53","author":"Wu","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2014.09.012","article-title":"A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion","volume":"156","author":"Gevaert","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.rse.2017.05.011","article-title":"Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps","volume":"196","author":"Li","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2015.11.016","article-title":"A flexible spatiotemporal method for fusing satellite images with different resolutions","volume":"172","author":"Zhu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"111537","DOI":"10.1016\/j.rse.2019.111537","article-title":"SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion","volume":"237","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111973","DOI":"10.1016\/j.rse.2020.111973","article-title":"FSDAF 2.0: Improving the performance of retrieving land cover changes and preserving spatial details","volume":"248","author":"Guo","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.ins.2014.10.006","article-title":"Towards building a data-intensive index for big data computing\u2014A case study of Remote Sensing data processing","volume":"319","author":"Ma","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1109\/JSTARS.2018.2797894","article-title":"Spatiotemporal Satellite Image Fusion Using Deep Convolutional Neural Networks","volume":"11","author":"Song","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tan, Z., Yue, P., Di, L., and Tang, J. (2018). Deriving High Spatiotemporal Remote Sensing Images Using Deep Convolutional Network. Remote Sens., 10.","DOI":"10.3390\/rs10071066"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tan, Z., Di, L., Zhang, M., Guo, L., and Gao, M. (2019). An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion. Remote Sens., 11.","DOI":"10.3390\/rs11242898"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6552","DOI":"10.1109\/TGRS.2019.2907310","article-title":"StfNet: A Two-Stream Convolutional Neural Network for Spatiotemporal Image Fusion","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"140302","DOI":"10.1007\/s11432-019-2805-y","article-title":"A new sensor bias-driven spatio-temporal fusion model based on convolutional neural networks","volume":"63","author":"Li","year":"2020","journal-title":"Sci. China Inf. Sci."},{"key":"ref_47","first-page":"102611","article-title":"Explicit and stepwise models for spatiotemporal fusion of remote sensing images with deep neural networks","volume":"105","author":"Ma","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8873079:1","DOI":"10.1155\/2020\/8873079","article-title":"Spatiotemporal Fusion of Remote Sensing Image Based on Deep Learning","volume":"2020","author":"Wang","year":"2020","journal-title":"J. Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"12190","DOI":"10.1109\/JSEN.2020.3000249","article-title":"DMNet: A network architecture using dilated convolution and multiscale mechanisms for spatiotemporal fusion of remote sensing images","volume":"20","author":"Li","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Peng, M., Zhang, L., Sun, X., Cen, Y., and Zhao, X. (2020). A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset. Remote Sens., 12.","DOI":"10.3390\/rs12233888"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4410816","DOI":"10.1109\/TGRS.2022.3169916","article-title":"MLFF-GAN: A Multi-Level Feature Fusion with GAN for Spatiotemporal Remote Sensing Images","volume":"60","author":"Song","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015\u201418th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"11879","DOI":"10.1109\/JSTARS.2021.3126645","article-title":"Attention_FPNet: Two-Branch Remote Sensing Image Pansharpening Network Based on Attention Feature Fusion","volume":"14","author":"Zhong","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","unstructured":"Luo, P., Ren, J., Peng, Z., Zhang, R., and Li, J. (2019, January 6\u20139). Differentiable Learning-to-Normalize via Switchable Normalization. Proceedings of the 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA."},{"key":"ref_57","unstructured":"Nwankpa, C., Ijomah, W., Gachagan, A., and Marshall, S. (2018). Activation functions: Comparison of trends in practice and research for deep learning. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2013.02.007","article-title":"Assessing the accuracy of blending Landsat\u2014MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection","volume":"133","author":"Emelyanova","year":"2013","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1583\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:54:39Z","timestamp":1760122479000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1583"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,14]]},"references-count":58,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061583"],"URL":"https:\/\/doi.org\/10.3390\/rs15061583","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,14]]}}}