{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T08:50:13Z","timestamp":1782291013171,"version":"3.54.5"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Natural Science Foundation of Shandong Province of China","award":["ZR2023QF096"],"award-info":[{"award-number":["ZR2023QF096"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62502182"],"award-info":[{"award-number":["62502182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s12652-026-05097-0","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:04:04Z","timestamp":1780315444000},"page":"1195-1211","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid multi-stream network for pansharpening based on transformer and convolution"],"prefix":"10.1007","volume":"17","author":[{"given":"Jiarui","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongtao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zilong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minglei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"5097_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115996","volume":"188","author":"T Benzenati","year":"2022","unstructured":"Benzenati T, Kessentini Y, Kallel A (2022) Pansharpening approach via two-stream detail injection based on relativistic generative adversarial networks. Expert Syst Appl 188:115996","journal-title":"Expert Syst Appl"},{"issue":"6","key":"5097_CR2","doi-asserted-by":"publisher","first-page":"5206","DOI":"10.1109\/TGRS.2020.3015878","volume":"59","author":"J Cai","year":"2020","unstructured":"Cai J, Huang B (2020) Super-resolution-guided progressive pansharpening based on a deep convolutional neural network. IEEE Trans Geosci Remote Sens 59(6):5206\u20135220","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5097_CR3","unstructured":"Cao S, Deng L, Deng S (2025) Respandiff: diffusion model for pansharpening by inferring residual inference. arXiv preprint arXiv:2501.05091"},{"key":"5097_CR4","unstructured":"Cui Y, Liu P, Zhang H (2025) Empowering your pansharpening models with generalizability: unified distribution is all you need. Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 11850\u201311860"},{"issue":"8","key":"5097_CR5","doi-asserted-by":"publisher","first-page":"6995","DOI":"10.1109\/TGRS.2020.3031366","volume":"59","author":"LJ Deng","year":"2020","unstructured":"Deng LJ, Vivone G, Jin C et al (2020) Detail injection-based deep convolutional neural networks for pansharpening. IEEE Trans Geosci Remote Sens 59(8):6995\u20137010","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"5097_CR6","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong C, Loy CC, He K et al (2015) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295\u2013307","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5097_CR7","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A (2021) An image is worth 16x16 words: transformers for image recognition at scale. Proceedings of the International Conference on Learning Representations (ICLR)"},{"key":"5097_CR8","doi-asserted-by":"publisher","first-page":"6325","DOI":"10.1109\/JSTARS.2023.3292320","volume":"16","author":"Y Fang","year":"2023","unstructured":"Fang Y, Cai Y, Fan L (2023) SDRCNN: a single-scale dense residual connected convolutional neural network for pansharpening. IEEE J Select Topics Appl Earth Observat Remote Sens 16:6325\u20136338","journal-title":"IEEE J Select Topics Appl Earth Observat Remote Sens"},{"key":"5097_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.129212","volume":"620","author":"Y Ge","year":"2025","unstructured":"Ge Y, Zhang X, Li X et al (2025) An interpretable bilateral detail optimization deep unfolding network for pansharpening. Neurocomputing 620:129212","journal-title":"Neurocomputing"},{"issue":"4","key":"5097_CR10","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1109\/JSTARS.2019.2898574","volume":"12","author":"L He","year":"2019","unstructured":"He L, Rao Y, Li J et al (2019) Pansharpening via detail injection based convolutional neural networks. IEEE J Select Topics Appl Earth Observat Remote Sen 12(4):1188\u20131204","journal-title":"IEEE J Select Topics Appl Earth Observat Remote Sen"},{"key":"5097_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102779","volume":"115","author":"X He","year":"2025","unstructured":"He X, Cao K, Zhang J et al (2025) Pan-mamba: effective pan-sharpening with state space model. Inform Fus 115:102779","journal-title":"Inform Fus"},{"key":"5097_CR12","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.neucom.2022.07.071","volume":"506","author":"S Hou","year":"2022","unstructured":"Hou S, Xiao S, Dong W et al (2022) Multi-level features fusion via cross-layer guided attention for hyperspectral pansharpening. Neurocomputing 506:380\u2013392","journal-title":"Neurocomputing"},{"key":"5097_CR13","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.comcom.2024.01.032","volume":"217","author":"P Jia","year":"2024","unstructured":"Jia P, Chen C, Zhang D et al (2024) Semantic segmentation of deep learning remote sensing images based on band combination principle: application in urban planning and land use. Comput Commun 217:97\u2013106","journal-title":"Comput Commun"},{"key":"5097_CR14","doi-asserted-by":"crossref","unstructured":"Kim S, Do J, Lee J (2025) U-know-diffpan: an uncertainty-aware knowledge distillation diffusion framework with details enhancement for pan-sharpening. Proceedings of the Computer Vision and Pattern Recognition Conference. pp 23069\u201323079","DOI":"10.1109\/CVPR52734.2025.02148"},{"issue":"1","key":"5097_CR15","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1007\/s41976-024-00183-3","volume":"8","author":"US Kumar","year":"2025","unstructured":"Kumar US, Kapali BSC, Nageswaran A et al (2025) Fusion of mobilenet and GRU: enhancing remote sensing applications for sustainable agriculture and food security. Remote Sens Earth Syst Sci 8(1):118\u2013131","journal-title":"Remote Sens Earth Syst Sci"},{"issue":"2","key":"5097_CR16","doi-asserted-by":"publisher","DOI":"10.1117\/1.JRS.14.026523","volume":"14","author":"W Lang","year":"2020","unstructured":"Lang W, Zhao Z, Fang S et al (2020) Sparse representation-based detail-injection method for pan-sharpening. J Appl Remote Sens 14(2):026523","journal-title":"J Appl Remote Sens"},{"key":"5097_CR17","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.isprsjprs.2025.04.001","volume":"225","author":"L Li","year":"2025","unstructured":"Li L, Han L, Ye Y et al (2025) Deep learning in remote sensing image matching: a survey. ISPRS J Photogramm Remote Sens 225:88\u2013112","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"5097_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124827","volume":"255","author":"N Li","year":"2024","unstructured":"Li N, Cheng L, Wang L et al (2024) Automatic labelling framework for optical remote sensing object detection samples in a wide area using deep learning. Expert Syst Appl 255:124827","journal-title":"Expert Syst Appl"},{"key":"5097_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123589","volume":"249","author":"Z Li","year":"2024","unstructured":"Li Z, Yuan G, Li J (2024) DUCD: deep unfolding convolutional-dictionary network for pansharpening remote sensing image. Expert Syst Appl 249:123589","journal-title":"Expert Syst Appl"},{"key":"5097_CR20","doi-asserted-by":"crossref","unstructured":"Liang L, Li T, Wang G, et\u00a0al (2025) Unlocking spatial textures: gradient-guided pansharpening for enhancing multispectral imagery. Neurocomputing 131607","DOI":"10.1016\/j.neucom.2025.131607"},{"key":"5097_CR21","doi-asserted-by":"crossref","unstructured":"Liu Q, Wang T, Jin Q, et\u00a0al (2025a) Bi-domain fusion pyramid network for pansharpening with deep anisotropic diffusion. Inform Fus 103212","DOI":"10.1016\/j.inffus.2025.103212"},{"key":"5097_CR22","doi-asserted-by":"crossref","unstructured":"Liu Y, Sun K, Liu Y, et\u00a0al (2025b) Spatial\u2013frequency domain aggregation upsampling for pan-sharpening. Neur Networks 108007","DOI":"10.1016\/j.neunet.2025.108007"},{"issue":"7","key":"5097_CR23","doi-asserted-by":"publisher","first-page":"594","DOI":"10.3390\/rs8070594","volume":"8","author":"G Masi","year":"2016","unstructured":"Masi G, Cozzolino D, Verdoliva L et al (2016) Pansharpening by convolutional neural networks. Remote Sensing 8(7):594","journal-title":"Remote Sensing"},{"key":"5097_CR24","unstructured":"Micikevicius P, Narang S, Alben J (2017) Mixed precision training. arXiv preprint arXiv:1710.03740"},{"key":"5097_CR25","doi-asserted-by":"publisher","first-page":"5468","DOI":"10.1109\/JSTARS.2022.3188181","volume":"15","author":"J Ni","year":"2022","unstructured":"Ni J, Shao Z, Zhang Z et al (2022) LDP-net: an unsupervised pansharpening network based on learnable degradation processes. IEEE J Sel Top Appl Earth Obs Remote Sens 15:5468\u20135479","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"issue":"3","key":"5097_CR26","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1109\/36.763274","volume":"37","author":"J Nunez","year":"2002","unstructured":"Nunez J, Otazu X, Fors O et al (2002) Multiresolution-based image fusion with additive wavelet decomposition. IEEE Trans Geosci Remote Sens 37(3):1204\u20131211","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5097_CR27","doi-asserted-by":"crossref","unstructured":"Peng S, Guo C, Wu X (2023) U2net: a general framework with spatial-spectral-integrated double u-net for image fusion. Proceedings of the 31st ACM International Conference on Multimedia. pp 3219\u20133227","DOI":"10.1145\/3581783.3612084"},{"key":"5097_CR28","doi-asserted-by":"crossref","unstructured":"Que Y, Yi Q, Xiong H (2025) Gciformer: an efficient global context injection transformer for remote sensing pansharpening. IEEE J Select Topics Appl Earth Observat Remote Sens","DOI":"10.1109\/JSTARS.2025.3623393"},{"key":"5097_CR29","doi-asserted-by":"crossref","unstructured":"Sun S, Xian M, Yao T (2025) Guided and variance-corrected fusion with one-shot style alignment for large-content image generation. Proceedings of the AAAI Conference on Artificial Intelligence. pp 7114\u20137121","DOI":"10.1609\/aaai.v39i7.32764"},{"key":"5097_CR30","unstructured":"Vaswani A, Shazeer N, Parmar N (2017) Attention is all you need. Adv Neur Inform Process Syst 30"},{"key":"5097_CR31","doi-asserted-by":"crossref","unstructured":"Wang J, Chen X, Huang X, et\u00a0al (2025a) Rethinking the role of panchromatic images in pan-sharpening. IEEE Trans Multimed","DOI":"10.1109\/TMM.2025.3535309"},{"key":"5097_CR32","doi-asserted-by":"crossref","unstructured":"Wang X, Zheng Z, Shao J, et\u00a0al (2025b) Adaptive rectangular convolution for remote sensing pansharpening. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp 17872\u201317881","DOI":"10.1109\/CVPR52734.2025.01665"},{"key":"5097_CR33","unstructured":"Wu H, Sun Z, Qi J, et\u00a0al (2025a) Spatial-spectral cross mamba network for hyperspectral and multispectral image fusion. IEEE Trans Geosci Remote Sens"},{"key":"5097_CR34","doi-asserted-by":"crossref","unstructured":"Wu R, Zhang Z, Deng S, et\u00a0al (2025b) Panadapter: two-stage fine-tuning with spatial-spectral priors injecting for pansharpening. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 8450\u20138459","DOI":"10.1609\/aaai.v39i8.32912"},{"key":"5097_CR35","doi-asserted-by":"publisher","first-page":"5496","DOI":"10.1109\/TIP.2024.3461476","volume":"33","author":"Y Xing","year":"2024","unstructured":"Xing Y, Qu L, Zhang S (2024) Crossdiff: exploring self-supervised representation of pansharpening via cross-predictive diffusion model. IEEE Trans Image Process 33:5496\u20135509","journal-title":"IEEE Trans Image Process"},{"issue":"11","key":"5097_CR36","doi-asserted-by":"publisher","first-page":"7380","DOI":"10.1109\/TGRS.2014.2311815","volume":"52","author":"Q Xu","year":"2014","unstructured":"Xu Q, Li B, Zhang Y et al (2014) High-fidelity component substitution pansharpening by the fitting of substitution data. IEEE Trans Geosci Remote Sens 52(11):7380\u20137392","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5097_CR37","doi-asserted-by":"crossref","unstructured":"Yang J, Fu X, Hu Y (2017) Pannet: a deep network architecture for pan-sharpening. Proceedings of the IEEE International Conference on Computer Vision. pp 5449\u20135457","DOI":"10.1109\/ICCV.2017.193"},{"key":"5097_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.128278","volume":"289","author":"K Yu","year":"2025","unstructured":"Yu K, Fu Y, Liu L et al (2025) Transformer gate-based interactive u-net for hyperspectral and multispectral image fusion. Expert Syst Appl 289:128278","journal-title":"Expert Syst Appl"},{"key":"5097_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127484","volume":"580","author":"W Zeng","year":"2024","unstructured":"Zeng W, Cheng M, Yuan Z et al (2024) Domain adaptive remote sensing image semantic segmentation with prototype guidance. Neurocomputing 580:127484","journal-title":"Neurocomputing"},{"key":"5097_CR40","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.inffus.2022.12.026","volume":"93","author":"K Zhang","year":"2023","unstructured":"Zhang K, Zhang F, Wan W et al (2023) Panchromatic and multispectral image fusion for remote sensing and earth observation: concepts, taxonomy, literature review, evaluation methodologies and challenges ahead. Information Fusion 93:227\u2013242","journal-title":"Information Fusion"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-026-05097-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-026-05097-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-026-05097-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T08:33:05Z","timestamp":1782289985000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-026-05097-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":40,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["5097"],"URL":"https:\/\/doi.org\/10.1007\/s12652-026-05097-0","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"8 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}