{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T11:14:32Z","timestamp":1779102872558,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T00:00:00Z","timestamp":1778371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R&D Program of Zhejiang","award":["No. 2024C03234"],"award-info":[{"award-number":["No. 2024C03234"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>High-resolution hyperspectral computational imaging is critical for applications such as environmental monitoring, urban planning, and precision agriculture. In practical hyperspectral imaging systems, physical hardware constraints inevitably lead to coupled degradations across spatial and spectral dimensions, making it difficult to simultaneously achieve high spatial resolution and high spectral fidelity. As a representative and widely studied hyperspectral computational imaging task, hyperspectral pansharpening aims to reconstruct high-resolution hyperspectral images by integrating low-resolution hyperspectral images with high-resolution panchromatic images. Existing methods frequently suffer from spectral distortion or blurred spatial details due to unidirectional fusion strategies or isolated processing branches that inadequately model the intrinsic spatial\u2013spectral coupling in the imaging process. To overcome these limitations, we propose a bidirectional driving framework that enables synergistic mutual guidance between spatial detail infusion and spectral fidelity preservation. Specifically, spatial coordinate-aware representations are dynamically integrated into a spectral self-attention module, while spectral importance scores are utilized to modulate multi-receptive-field convolutions via channel-wise weighting. This bidirectional interaction mechanism forms a closed-loop coupling between spatial and spectral representations, ensuring enhanced spatial reconstruction while rigorously preserving spectral integrity. Furthermore, to bridge the gap between simulated experiments and real-world applications, we constructed a large-scale dataset derived from the ZY-1-02D satellite. This dataset features high-fidelity PAN (17,820 \u00d7 16,128) and HSI (1485 \u00d7 1344) pairs, which we have made publicly available to the community to facilitate future research. Extensive experiments on both benchmark simulations and the proposed ZY-1-02D dataset demonstrate that our method achieves state-of-the-art performance in both spatial fidelity and spectral preservation.<\/jats:p>","DOI":"10.3390\/s26103009","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T15:39:32Z","timestamp":1778513972000},"page":"3009","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatial\u2013Spectral Bidirectional-Driven Collaborative Network with Coordinate-Aware and Spectral-Modulated Interaction for Hyperspectral Pansharpening"],"prefix":"10.3390","volume":"26","author":[{"given":"Qingshan","family":"Gao","sequence":"first","affiliation":[{"name":"Siwei SuperView Satellite Remote Sensing Co., Ltd., Hangzhou 310012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Conghui","family":"Tao","sequence":"additional","affiliation":[{"name":"Siwei SuperView Satellite Remote Sensing Co., Ltd., Hangzhou 310012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiongjun","family":"Du","sequence":"additional","affiliation":[{"name":"Siwei SuperView Satellite Remote Sensing Co., Ltd., Hangzhou 310012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanmin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Siwei SuperView Satellite Remote Sensing Co., Ltd., Hangzhou 310012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shixuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Eco-Environmental Science Research & Design Institute of Zhejiang Province, Hangzhou 310007, China"},{"name":"State Environmental Protection Engineering (Zhejiang) Center for Water Pollution Control, Hangzhou 310007, China"},{"name":"Key Laboratory of Environmental Pollution Control Technology of Zhejiang Province, Hangzhou 310007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuxiu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Spatial Planning and Design, Hangzhou City University, Hangzhou 310015, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1331-4917","authenticated-orcid":false,"given":"Qiuxiao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Spatial Planning and Design, Hangzhou City University, Hangzhou 310015, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, P., Li, X., Zhang, R., Gu, Q., Zhang, L., and Lv, J. (2025). Self-Supervised Hierarchical Dilated Transformer Network for Hyperspectral Soil Microplastic Identification and Detection. Sensors, 25.","DOI":"10.3390\/s25216517"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Weber, C., Aguejdad, R., Briottet, X., Avala, J., Fabre, S., Demuynck, J., Zenou, E., Deville, Y., Karoui, M., and Benhalouche, F. (2018). Hyperspectral imagery for environmental urban planning. IEEE International Geoscience and Remote Sensing Symposium, IEEE.","DOI":"10.1109\/IGARSS.2018.8519085"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pankajakshan, P., Padmasanan, A., and Sundar, S. (2026). Deep Architectures Fail to Generalize: A Lightweight Alternative for Agricultural Domain Transfer in Hyperspectral Images. Sensors, 26.","DOI":"10.20944\/preprints202511.1978.v1"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, H., Liang, H., and Wang, Q. (2026). Degradation-Aware Dynamic Kernel Generation Network for Hyperspectral Super-Resolution. Sensors, 26.","DOI":"10.3390\/s26041362"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1364\/JOSAA.36.001917","article-title":"Local model-based hyperspectral pansharpening algorithm via optimization constraint equation and sliding window","volume":"36","author":"Dong","year":"2019","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"024504","DOI":"10.1117\/1.JRS.17.024504","article-title":"Image fusion of hyperspectral and multispectral imagery using nearest-neighbor diffusion","volume":"17","author":"Ducay","year":"2023","journal-title":"J. Appl. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5401915","DOI":"10.1109\/TGRS.2023.3265404","article-title":"A novel spatial fidelity with learnable nonlinear map for panchromatic sharpening","volume":"61","author":"Wen","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5898","DOI":"10.1109\/JSTARS.2020.3025040","article-title":"Spectral-fidelity convolutional neural networks for hyperspectral pansharpening","volume":"13","author":"He","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7303","DOI":"10.1109\/TNNLS.2021.3084745","article-title":"Generative dual-adversarial network with spectral fidelity and spatial enhancement for hyperspectral pansharpening","volume":"33","author":"Dong","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bandara, W.G.C., and Patel, V.M. (2022). Hypertransformer: A textural and spectral feature fusion transformer for pansharpening. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE\/CVF.","DOI":"10.1109\/CVPR52688.2022.00181"},{"key":"ref_11","first-page":"5402420","article-title":"CADUI: Cross-attention-based depth unfolding iteration network for pansharpening remote sensing images","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1109\/TIP.2023.3244414","article-title":"Can Spectral Information Work While Extracting Spatial Distribution?\u2014An Online Spectral Information Compensation Network for HSI Classification","volume":"32","author":"Yang","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Vivone, G., Restaino, R., Licciardi, G., Mura, M.D., and Chanussot, J. (2014). Multiresolution analysis and component substitution techniques for hyperspectral pansharpening. IEEE Geoscience and Remote Sensing Symposium, IEEE.","DOI":"10.1109\/IGARSS.2014.6947018"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7380","DOI":"10.1109\/TGRS.2014.2311815","article-title":"High-fidelity component substitution pansharpening by the fitting of substitution data","volume":"52","author":"Xu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2867","DOI":"10.1109\/JSTARS.2017.2697445","article-title":"Combining component substitution and multiresolution analysis: A novel generalized BDSD pansharpening algorithm","volume":"10","author":"Zhong","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Han, X.-H., Shi, B., and Zheng, Y. (2018). Residual HSRCNN: Residual hyper-spectral reconstruction CNN from an RGB image. International Conference on Pattern Recognition, IEEE.","DOI":"10.1109\/ICPR.2018.8545634"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9697","DOI":"10.1109\/TGRS.2021.3054641","article-title":"SC-PNN: Saliency cascade convolutional neural network for pansharpening","volume":"59","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6995","DOI":"10.1109\/TGRS.2020.3031366","article-title":"Detail injection-based deep convolutional neural networks for pansharpening","volume":"59","author":"Deng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","first-page":"5518413","article-title":"A dual-branch detail extraction network for hyperspectral pansharpening","volume":"60","author":"Qu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5542016","DOI":"10.1109\/TGRS.2022.3217542","article-title":"A spatial\u2013spectral dual-optimization model-driven deep network for hyperspectral and multispectral image fusion","volume":"60","author":"Dong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3092","DOI":"10.1109\/JSTARS.2019.2917584","article-title":"HyperPNN: Hyperspectral pansharpening via spectrally predictive convolutional neural networks","volume":"12","author":"He","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2090","DOI":"10.1109\/TNNLS.2020.2996498","article-title":"Deep multiscale detail networks for multiband spectral image sharpening","volume":"32","author":"Fu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.1109\/TNNLS.2022.3189049","article-title":"A spatio-spectral fusion method for hyperspectral images using residual hyper-dense network","volume":"35","author":"Qu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107324","DOI":"10.1016\/j.knosys.2021.107324","article-title":"Channel\u2013spatial attention-based pan-sharpening of very high-resolution satellite images","volume":"229","author":"Wang","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"042605","DOI":"10.1117\/1.JRS.18.042605","article-title":"TSSA-Net: Two-stream self-attention network for hyperspectral image super-resolution","volume":"18","author":"Xu","year":"2024","journal-title":"J. Appl. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2353290","DOI":"10.1080\/22797254.2024.2353290","article-title":"A multi-scale multi-channel CNN introducing a channel-spatial attention mechanism hyperspectral remote sensing image classification method","volume":"57","author":"Zhao","year":"2024","journal-title":"Eur. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9088","DOI":"10.1109\/TNNLS.2022.3155655","article-title":"A triple-double convolutional neural network for panchromatic sharpening","volume":"34","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhou, H., Liu, Q., and Wang, Y. (2022). PanFormer: A transformer based model for pan-sharpening. IEEE International Conference on Multimedia and Expo, IEEE.","DOI":"10.1109\/ICME52920.2022.9859770"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"16733","DOI":"10.1109\/TNNLS.2023.3297319","article-title":"Spectral\u2013spatial transformer for hyperspectral image sharpening","volume":"35","author":"Chen","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ye, H., and He, L. (2024). Adaptively Spatial-Spectral Transformer Network for HS Pansharpening. Chinese Control Conference, IEEE.","DOI":"10.23919\/CCC63176.2024.10662342"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6427","DOI":"10.1109\/TPAMI.2025.3557581","article-title":"HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model","volume":"47","author":"Wang","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TIP.2023.3326699","article-title":"ITER: Image-to-Pixel Representation for Weakly Supervised HSI Classification","volume":"33","author":"Yang","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"14467","DOI":"10.1109\/TNNLS.2023.3279377","article-title":"Overcoming the Barrier of Incompleteness: A Hyperspectral Image Classification Full Model","volume":"35","author":"Yang","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_35","first-page":"5515214","article-title":"Multistage dual-attention guided fusion network for hyperspectral pansharpening","volume":"60","author":"Guan","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5512805","DOI":"10.1109\/LGRS.2022.3170904","article-title":"PMACNet: Parallel multiscale attention constraint network for pan-sharpening","volume":"19","author":"Liang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1109\/MGRS.2022.3187652","article-title":"Machine learning in pansharpening: A benchmark, from shallow to deep networks","volume":"10","author":"Deng","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Xu, S., Zhang, J., Zhao, Z., Sun, K., Liu, J., and Zhang, C. (2021). Deep gradient projection networks for pan-sharpening. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE\/CVF.","DOI":"10.1109\/CVPR46437.2021.00142"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhao, C.-Y., Zhang, T.-J., Ran, R., Chen, Z.-X., and Deng, L.-J. (2023). LGPConv: Learnable Gaussian Perturbation Convolution for Lightweight Pansharpening. International Joint Conference on Artificial Intelligence, IJCAI.","DOI":"10.24963\/ijcai.2023\/517"},{"key":"ref_40","first-page":"5419","article-title":"Deep unfolded network with intrinsic supervision for pan-sharpening","volume":"38","author":"Wang","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"131607","DOI":"10.1016\/j.neucom.2025.131607","article-title":"Unlocking spatial textures: Gradient-guided pansharpening for enhancing multispectral imagery","volume":"657","author":"Liang","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"108754","DOI":"10.1016\/j.optlaseng.2024.108754","article-title":"Dual-domain deep unfolding Transformer for spectral compressive imaging reconstruction","volume":"186","author":"Zhou","year":"2025","journal-title":"Opt. Lasers Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/10\/3009\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T10:26:50Z","timestamp":1779100010000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/10\/3009"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,10]]},"references-count":42,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["s26103009"],"URL":"https:\/\/doi.org\/10.3390\/s26103009","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,10]]}}}