{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T20:13:03Z","timestamp":1771272783058,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Special Science and Technology Project of Yunnan Province","award":["202202AE09002105"],"award-info":[{"award-number":["202202AE09002105"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Pan-sharpening aims to generate high-resolution multispectral (HRMS) images by combining high-resolution panchromatic (PAN) images with low-resolution multispectral (LRMS) data, while maintaining the symmetry of spatial and spectral characteristics. Traditional convolutional neural networks (CNNs) struggle with global dependency modeling due to local receptive fields, and Transformer-based models are computationally expensive. Recent Mamba models offer linear complexity and effective global modeling. However, existing Mamba-based methods lack sensitivity to local feature variations, leading to suboptimal fine-detail preservation. To address this, we propose a Conditional Skipping Mamba Network (CSMN), which enhances global-local feature fusion symmetrically through two modules: (1) the Adaptive Mamba Module (AMM), which improves global perception using adaptive spatial-frequency integration; and (2) the Cross-domain Mamba Module (CDMM), optimizing cross-domain spectral-spatial representation. Experimental results on the IKONOS and WorldView-2 datasets demonstrate that CSMN surpasses existing state-of-the-art methods in achieving superior spectral consistency and preserving spatial details, with performance that is more symmetric in fine-detail preservation.<\/jats:p>","DOI":"10.3390\/sym16121681","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T03:59:43Z","timestamp":1734580783000},"page":"1681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Conditional Skipping Mamba Network for Pan-Sharpening"],"prefix":"10.3390","volume":"16","author":[{"given":"Yunxuan","family":"Tang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0452-6248","authenticated-orcid":false,"given":"Huaguang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Big Data, Yunnan Agricultural University, Kunming 650201, China"},{"name":"The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Yunnan Agricultural University, Kunming 650201, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1109\/JSTARS.2022.3148139","article-title":"Progress and challenges in intelligent remote sensing satellite systems","volume":"15","author":"Zhang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1038\/s43017-022-00373-x","article-title":"Landslide detection, monitoring and prediction with remote-sensing techniques","volume":"4","author":"Casagli","year":"2023","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_3","first-page":"691","article-title":"Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images","volume":"63","author":"Wald","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1109\/LGRS.2016.2597271","article-title":"Nonlinear IHS: A promising method for pan-sharpening","volume":"13","author":"Ghahremani","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","unstructured":"Sebastian, R. (2016). An overview of gradient descent optimization algorithms. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1109\/TIP.2021.3137020","article-title":"A unified pansharpening model based on band-adaptive gradient and detail correction","volume":"31","author":"Lu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.inffus.2022.10.010","article-title":"P2Sharpen: A progressive pansharpening network with deep spectral transformation","volume":"91","author":"Zhang","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xu, S., Zhang, J., Zhao, Z., Sun, K., Liu, J., and Zhang, C. (2021, January 20\u201325). Deep gradient projection networks for pan-sharpening. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00142"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhou, H., Liu, Q., and Wang, Y. (2022, January 18\u201322). PanFormer: A transformer based model for pan-sharpening. Proceedings of the 2022 IEEE International Conference on Multimedia and Expo (ICME), Taipei, Taiwan.","DOI":"10.1109\/ICME52920.2022.9859770"},{"key":"ref_10","unstructured":"Zhou, M., Huang, J., Fang, Y., Fu, X., and Liu, A. (March, January 22). Pan-sharpening with customized transformer and invertible neural network. Proceedings of the AAAI Conference on Artificial Intelligence, Online."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhou, M., Huang, J., Yan, K., Yu, H., Fu, X., Liu, A., Wei, X., and Zhao, F. (2022, January 23\u201327). Spatial-frequency domain information integration for pan-sharpening. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19797-0_16"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1109\/JSTARS.2018.2794888","article-title":"A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening","volume":"11","author":"Yuan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","unstructured":"Laben, C.A., and Brower, B.V. (2000). Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening. (6,011,875), U.S. Patent."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., and Timofte, R. (2021, January 11\u201317). Swinir: Image restoration using swin transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, X., Cao, K., Yan, K., Li, R., Xie, C., Zhang, J., and Zhou, M. (2024). Pan-mamba: Effective pan-sharpening with state space model. arXiv.","DOI":"10.1016\/j.inffus.2024.102779"},{"key":"ref_16","unstructured":"Liu, Y., Tian, Y., Zhao, Y., Yu, H., Xie, L., Wang, Y., Ye, Q., and Liu, Y. (2024). VMamba: Visual State Space Model. arXiv."},{"key":"ref_17","unstructured":"Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., and Wang, X. (2024). Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv."},{"key":"ref_18","first-page":"5402216","article-title":"Spatial-spectral dual back-projection network for pansharpening","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9358","DOI":"10.1109\/JSTARS.2023.3298995","article-title":"MPEFNet: Multilevel Progressive Enhancement Fusion Network for Pansharpening","volume":"16","author":"Li","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","first-page":"5406015","article-title":"Learning deep multiscale local dissimilarity prior for pansharpening","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TGRS.2010.2051674","article-title":"A new adaptive component-substitution-based satellite image fusion by using partial replacement","volume":"49","author":"Choi","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"1325","article-title":"Reconstruction of multispatial, multispectral image data using spatial frequency content","volume":"46","author":"Schowengerdt","year":"1980","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2300","DOI":"10.1109\/TGRS.2002.803623","article-title":"Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis","volume":"40","author":"Aiazzi","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s11263-006-6852-x","article-title":"A variational model for P+ XS image fusion","volume":"69","author":"Ballester","year":"2006","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","first-page":"5504615","article-title":"Diffused convolutional neural network for hyperspectral image super-resolution","volume":"61","author":"Jia","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","unstructured":"Peng, S., Guo, C., Wu, X., and Deng, L.J. (November, January 29). U2net: A general framework with spatial-spectral-integrated double u-net for image fusion. Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, ON, Canada."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, J., Fu, X., Hu, Y., Huang, Y., Ding, X., and Paisley, J. (2017, January 22\u201329). PanNet: A deep network architecture for pan-sharpening. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.193"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4407613","DOI":"10.1109\/TGRS.2023.3309949","article-title":"Novel land-cover classification approach with nonparametric sample augmentation for hyperspectral remote sensing images","volume":"61","author":"Lv","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Qiao, S., Chen, L.C., and Yuille, A. (2021, January 20\u201325). Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01008"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5403418","DOI":"10.1109\/TGRS.2023.3280647","article-title":"Multispectral remote sensing image deblurring using auxiliary band gradient information","volume":"61","author":"Liao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","unstructured":"Gu, A., Goel, K., and R\u00e9, C. (2021). Efficiently modeling long sequences with structured state spaces. arXiv."},{"key":"ref_33","unstructured":"Gu, A., and Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv."},{"key":"ref_34","unstructured":"Mehta, H., Gupta, A., Cutkosky, A., and Neyshabur, B. (2022). Long range language modeling via gated state spaces. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5403213","DOI":"10.1109\/TGRS.2023.3273334","article-title":"Multiscale dual-domain guidance network for pan-sharpening","volume":"61","author":"He","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","first-page":"572","article-title":"Combining recurrent, convolutional, and continuous-time models with linear state space layers","volume":"34","author":"Gu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5517216","DOI":"10.1109\/TGRS.2024.3394533","article-title":"A Deep Multi-Resolution Representation Framework for Pansharpening","volume":"62","author":"Xie","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3012","DOI":"10.1109\/TGRS.2007.904923","article-title":"Comparison of pansharpening algorithms: Outcome of the 2006 GRS-S data-fusion contest","volume":"45","author":"Alparone","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12518-016-0179-2","article-title":"Evaluation of pan-sharpening methods for spatial and spectral quality","volume":"9","author":"Pushparaj","year":"2017","journal-title":"Appl. Geomat."},{"key":"ref_40","unstructured":"Yuhas, R.H., Goetz, A.F., and Boardman, J.W. (1992). Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm. Summaries of the Third Annual JPL Airborne Geoscience Workshop, JPL. Volume 1: AVIRIS Workshop."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/97.995823","article-title":"A universal image quality index","volume":"9","author":"Wang","year":"2002","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1109\/LGRS.2009.2022650","article-title":"Hypercomplex quality assessment of multi\/hyperspectral images","volume":"6","author":"Garzelli","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"193","DOI":"10.14358\/PERS.74.2.193","article-title":"Multispectral and panchromatic data fusion assessment without reference","volume":"74","author":"Alparone","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tang, Y., Li, H., Xie, G., Liu, P., and Li, T. (2024). Multi-Frequency Spectral\u2013Spatial Interactive Enhancement Fusion Network for Pan-Sharpening. Electronics, 13.","DOI":"10.3390\/electronics13142802"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/0034-4257(87)90088-5","article-title":"Color enhancement of highly correlated images. II. Channel ratio and \u201cchromaticity\u201d transformation techniques","volume":"22","author":"Gillespie","year":"1987","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TGRS.2014.2361734","article-title":"A critical comparison among pansharpening algorithms","volume":"53","author":"Vivone","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5549","DOI":"10.1109\/TGRS.2019.2900419","article-title":"Pan-sharpening using an efficient bidirectional pyramid network","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, Y., Deng, L.J., Zhang, T.J., and Wu, X. (2021, January 20\u201324). SSconv: Explicit spectral-to-spatial convolution for pansharpening. Proceedings of the 29th ACM International Conference on Multimedia, Virtual Event, China.","DOI":"10.1145\/3474085.3475600"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/12\/1681\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:55:17Z","timestamp":1760115317000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/12\/1681"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,19]]},"references-count":48,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["sym16121681"],"URL":"https:\/\/doi.org\/10.3390\/sym16121681","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,19]]}}}