{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T16:43:29Z","timestamp":1764002609681,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of China","award":["61972060","U1713213","62027827","2019YFE0110800","cstc2020jcyj-zdxmX0025","cstc2019cxcyljrc-td0270"],"award-info":[{"award-number":["61972060","U1713213","62027827","2019YFE0110800","cstc2020jcyj-zdxmX0025","cstc2019cxcyljrc-td0270"]}]},{"name":"National Key Research and Development Program of China","award":["61972060","U1713213","62027827","2019YFE0110800","cstc2020jcyj-zdxmX0025","cstc2019cxcyljrc-td0270"],"award-info":[{"award-number":["61972060","U1713213","62027827","2019YFE0110800","cstc2020jcyj-zdxmX0025","cstc2019cxcyljrc-td0270"]}]},{"name":"Natural Science Foundation of Chongqing","award":["61972060","U1713213","62027827","2019YFE0110800","cstc2020jcyj-zdxmX0025","cstc2019cxcyljrc-td0270"],"award-info":[{"award-number":["61972060","U1713213","62027827","2019YFE0110800","cstc2020jcyj-zdxmX0025","cstc2019cxcyljrc-td0270"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spatiotemporal fusion is an effective and cost-effective method to obtain both high temporal resolution and high spatial resolution images. However, existing methods do not sufficiently extract the deeper features of the image, resulting in fused images which do not recover good topographic detail and poor fusion quality. In order to obtain higher quality spatiotemporal fusion images, a novel spatiotemporal fusion method based on deep learning is proposed in this paper. The method combines an attention mechanism and a multiscale feature fusion network to design a network that more scientifically explores deeper features of the image for different input image characteristics. Specifically, a multiscale feature fusion module is introduced into the spatiotemporal fusion task and combined with an efficient spatial-channel attention module to improve the capture of spatial and channel information while obtaining more effective information. In addition, we design a new edge loss function and incorporate it into the compound loss function, which helps to generate fused images with richer edge information. In terms of both index performance and image details, our proposed model has excellent results on both datasets compared with the current mainstream spatiotemporal fusion methods.<\/jats:p>","DOI":"10.3390\/rs15010182","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T03:18:18Z","timestamp":1672370298000},"page":"182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Multiscale Spatiotemporal Fusion Network Based on an Attention Mechanism"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhiqiang","family":"Huang","sequence":"first","affiliation":[{"name":"College of Computer, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Yujia","family":"Li","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Menghao","family":"Bai","sequence":"additional","affiliation":[{"name":"College of Computer, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Qing","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Computer, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Qian","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Computer, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Zhijun","family":"Mou","sequence":"additional","affiliation":[{"name":"College of Computer, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7966-7227","authenticated-orcid":false,"given":"Liping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7482-6417","authenticated-orcid":false,"given":"Dajiang","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Computer, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.isprsjprs.2015.10.004","article-title":"Remote sensing platforms and sensors: A survey","volume":"115","author":"Toth","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111051","DOI":"10.1016\/j.rse.2019.01.013","article-title":"Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD+ reporting","volume":"238","author":"Olofsson","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.3390\/rs70201798","article-title":"Comparison of Spatiotemporal Fusion Models: A Review","volume":"7","author":"Chen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Belgiu, M., and Stein, A. (2019). Spatiotemporal Image Fusion in Remote Sensing. Remote Sens., 11.","DOI":"10.3390\/rs11070818"},{"key":"ref_6","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_7","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_8","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_9","doi-asserted-by":"crossref","first-page":"10174","DOI":"10.1109\/JSTARS.2021.3113163","article-title":"A Multi-Cooperative Deep Convolutional Neural Network for Spatiotemporal Satellite Image Fusion","volume":"14","author":"Li","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MGRS.2016.2637824","article-title":"Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature","volume":"5","author":"Yokoya","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_11","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_12","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_13","unstructured":"Chen, J., Pan, Y., and Chen, Y. (2020). Remote sensing image fusion based on Bayesian GAN. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1080\/2150704X.2013.769283","article-title":"Unified fusion of remote-sensing imagery: Generating simultaneously high-resolution synthetic spatial\u2013temporal\u2013spectral earth observations","volume":"4","author":"Huang","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_15","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_16","first-page":"1","article-title":"Spatiotemporal Reflectance Fusion via Tensor Sparse Representation","volume":"60","author":"Peng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Jia, D., Song, C., Cheng, C., Shen, S., Ning, L., and Hui, C. (2020). A Novel Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions Using a Two-Stream Convolutional Neural Network. Remote Sens., 12.","DOI":"10.3390\/rs12040698"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw. Off. J. Int. Neural Netw. Soc."},{"key":"ref_20","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_21","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_22","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":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1080\/19479832.2020.1727573","article-title":"An optimised multi-scale fusion method for airport detection in large-scale optical remote sensing images","volume":"11","author":"Yin","year":"2020","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1007\/s11554-021-01080-4","article-title":"Real-time and effective pan-sharpening for remote sensing using multi-scale fusion network","volume":"18","author":"Lai","year":"2021","journal-title":"J. Real Time Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, C., Chen, Y., Yang, X., Gao, S., Li, F., Kong, A., Zu, D., and Sun, L. (2020). Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion. Remote Sens., 12.","DOI":"10.3390\/rs12020213"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2013.02.007","article-title":"Assessing the accuracy of blending Landsat\u2013MODIS 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."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_28","unstructured":"Park, J., Woo, S., Lee, J.Y., and Kweon, I.S. (2018, January 3\u20136). BAM: Bottleneck Attention Module. Proceedings of the BMVC, Newcastle, UK."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the ECCV, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mei, Y., Fan, Y., and Zhou, Y. (2021, January 20\u201325). Image Super-Resolution with Non-Local Sparse Attention. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00352"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L. (2016). Perceptual Losses for Real-Time Style Transfer and Super-Resolution. arXiv.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1109\/TPAMI.2007.1027","article-title":"Laplacian Operator-Based Edge Detectors","volume":"29","author":"Wang","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1080\/01431161.2022.2030070","article-title":"Convolution neural network with edge structure loss for spatiotemporal remote sensing image fusion","volume":"43","author":"Lei","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tian, Q., Xie, G., Wang, Y., and Zhang, Y. (2018, January 13\u201315). Pedestrian detection based on laplace operator image enhancement algorithm and faster R-CNN. Proceedings of the 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Beijing, China.","DOI":"10.1109\/CISP-BMEI.2018.8633093"},{"key":"ref_35","unstructured":"Zhao, H., Gallo, O., Frosio, I., and Kautz, J. (2015). Loss Functions for Neural Networks for Image Processing. arXiv."},{"key":"ref_36","unstructured":"Yuhas, R.H., Goetz, A.F.H., and Boardman, J.W. (1992, January 1\u20135). Discrimination among semi-arid landscape endmembers using the Spectral Angle Mapper (SAM) algorithm. Proceedings of the Third Annual JPL Airborne Geoscience Workshop, Pasadena, CA, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3880","DOI":"10.1109\/TGRS.2009.2029094","article-title":"Pansharpening Quality Assessment Using the Modulation Transfer Functions of Instruments","volume":"47","author":"Khan","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/182\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:55:16Z","timestamp":1760147716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,29]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010182"],"URL":"https:\/\/doi.org\/10.3390\/rs15010182","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,12,29]]}}}