{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T08:52:53Z","timestamp":1773478373327,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T00:00:00Z","timestamp":1693008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41861055"],"award-info":[{"award-number":["41861055"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019M653795"],"award-info":[{"award-number":["2019M653795"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["201806"],"award-info":[{"award-number":["201806"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["41861055"],"award-info":[{"award-number":["41861055"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019M653795"],"award-info":[{"award-number":["2019M653795"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["201806"],"award-info":[{"award-number":["201806"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"LZJTU EP Program","award":["41861055"],"award-info":[{"award-number":["41861055"]}]},{"name":"LZJTU EP Program","award":["2019M653795"],"award-info":[{"award-number":["2019M653795"]}]},{"name":"LZJTU EP Program","award":["201806"],"award-info":[{"award-number":["201806"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral satellite imagery has developed rapidly over the last decade because of its high spectral resolution and strong material recognition capability. Nonetheless, the spatial resolution of available hyperspectral imagery is inferior, severely affecting the accuracy of ground object identification. In the paper, we propose an adaptively optimized pulse-coupled neural network (PCNN) model to sharpen the spatial resolution of the hyperspectral imagery to the scale of the multispectral imagery. Firstly, a SAM-CC strategy is designed to assign hyperspectral bands to the multispectral bands. Subsequently, an improved PCNN (IPCNN) is proposed, which considers the differences of the neighboring neurons. Furthermore, the Chameleon Swarm Optimization (CSA) optimization is adopted to generate the optimum fusion parameters for IPCNN. Hence, the injected spatial details are acquired in the irregular regions generated by the IPCNN. Extensive experiments are carried out to validate the superiority of the proposed model, which confirms that our method can realize hyperspectral imagery with high spatial resolution, yielding the best spatial details and spectral information among the state-of-the-art approaches. Several ablation studies further corroborate the efficiency of our method.<\/jats:p>","DOI":"10.3390\/rs15174205","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T05:46:47Z","timestamp":1693201607000},"page":"4205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening"],"prefix":"10.3390","volume":"15","author":[{"given":"Xinyu","family":"Xu","sequence":"first","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3410-8891","authenticated-orcid":false,"given":"Xiaojun","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China"}]},{"given":"Yikun","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0223-1094","authenticated-orcid":false,"given":"Lu","family":"Kang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China"}]},{"given":"Junfei","family":"Ge","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s41651-023-00136-2","article-title":"Hybrid Behrens-Fisher- and Gray Contrast\u2013Based Feature Point Selection for Building Detection from Satellite Images","volume":"7","author":"Kokila","year":"2023","journal-title":"J. Geovisualization Spat. Anal."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107049","DOI":"10.1016\/j.compag.2022.107049","article-title":"AFFU-Net: Attention feature fusion U-Net with hybrid loss for winter jujube crack detection","volume":"198","author":"Zheng","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2023.3249624","article-title":"Detection of Bastn\u00e4site-Rich Veins in Rare Earth Element Ores through Hyperspectral Imaging","volume":"20","author":"Gadea","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Boubanga-Tombet, S., Huot, A., Vitins, I., Heuberger, S., Veuve, C., Eisele, A., Hewson, R., Guyot, E., Marcotte, F., and Chamberland, M. (2018). Thermal Infrared Hyperspectral Imaging for Mineralogy Mapping of a Mine Face. Remote Sens., 10.","DOI":"10.3390\/rs10101518"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MGRS.2020.3019315","article-title":"A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening with Classical and Emerging Pansharpening Methods","volume":"9","author":"Vivone","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3609","DOI":"10.1080\/01431161.2022.2100726","article-title":"GRF: Guided Residual Fusion for Pansharpening","volume":"43","author":"Jiayuan","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1007\/s12524-017-0736-0","article-title":"Remote Sensing Image Fusion Method Based on PCA and Curvelet Transform","volume":"46","author":"Wu","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kong, Y., Hong, F., Leung, H., and Peng, X. (2021). A Fusion Method of Optical Image and SAR Image Based on Dense-UGAN and Gram\u2013Schmidt Transformation. Remote Sens., 13.","DOI":"10.3390\/rs13214274"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/TGRS.2007.907604","article-title":"Optimal MMSE pan sharpening of very high resolution multispectral images","volume":"46","author":"Garzelli","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6421","DOI":"10.1109\/TGRS.2019.2906073","article-title":"Robust Band-Dependent Spatial-Detail Approaches for Panchromatic Sharpening","volume":"57","author":"Vivone","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens. Publ. IEEE Geosci. Remote Sens. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1109\/JSTARS.2022.3232145","article-title":"Pansharpening Based on Adaptive High-Frequency Fusion and Injection Coefficients Optimization","volume":"16","author":"Yang","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.inffus.2020.11.001","article-title":"Recent advances and new guidelines on hyperspectral and multispectral image fusion","volume":"69","author":"Dian","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s41651-022-00130-0","article-title":"Comparing Pan-sharpened Landsat-9 and Sentinel-2 for Land-Use Classification Using Machine Learning Classifiers","volume":"6","author":"Bouslihim","year":"2022","journal-title":"J. Geovisualization Spat. Anal."},{"key":"ref_14","first-page":"671","article-title":"The Laplacian Pyramid as a Compact Image Code","volume":"31","author":"Burt","year":"1987","journal-title":"Read. Comput. Vis."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gao, W., Xiao, Z., and Bao, T. (2023). Detection and Identification of Potato-Typical Diseases Based on Multidimensional Fusion Atrous-CNN and Hyperspectral Data. Appl. Sci., 13.","DOI":"10.3390\/app13085023"},{"key":"ref_16","unstructured":"Jindal, H., Bharti, M., Kasana, S., and Saxena, S. An ensemble mosaicing and ridgelet based fusion technique for underwater panoramic image reconstruction and its refinement. Multimed. Tools Appl., Available online: https:\/\/link.springer.com\/article\/10.1007\/s11042-023-14594-9."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.1007\/s12524-018-0859-y","article-title":"Remote sensing image fusion based on nonlinear IHS and fast nonsubsampled contourlet transform","volume":"46","author":"Du","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.neucom.2012.10.025","article-title":"A novel algorithm of remote sensing image fusion based on Shearlets and PCNN","volume":"117","author":"Cheng","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2882","DOI":"10.1109\/TIP.2016.2556944","article-title":"Fusion of multispectral and panchromatic images based on morphological operators","volume":"25","author":"Restaino","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2104","DOI":"10.1080\/01431161.2018.1475778","article-title":"Research on the soil moisture sliding estimation method using the LS-SVM based on multi-satellite fusion","volume":"40","author":"Ren","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.inffus.2022.08.032","article-title":"Multispectral and hyperspectral image fusion in remote sensing: A survey","volume":"89","author":"Vivone","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_22","first-page":"36","article-title":"Wavelet-based hyperspectral and multispectral image fusion","volume":"4383","author":"Gomez","year":"2001","journal-title":"Proc. SPIE-Int. Soc. Opt. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1109\/LGRS.2013.2294476","article-title":"Fusion of Hyperspectral and Multispectral Images: A Novel Framework Based on Generalization of Pan-Sharpening Methods","volume":"11","author":"Chen","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1109\/LGRS.2017.2677087","article-title":"Band assignment approaches for hyperspectral sharpening","volume":"14","author":"Picone","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1534","DOI":"10.1109\/JSTARS.2019.2908984","article-title":"Hyper-sharpening based on spectral modulation","volume":"12","author":"Lu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/TGRS.2011.2161320","article-title":"Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion","volume":"50","author":"Yokoya","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4469","DOI":"10.1109\/TCYB.2019.2951572","article-title":"Nonlocal Sparse Tensor Factorization for Semiblind Hyperspectral and Multispectral Images Fusion","volume":"50","author":"Dian","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_28","first-page":"102926","article-title":"Deep learning in multimodal remote sensing data fusion: A comprehensive review","volume":"112","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, J., Yuan, Z., Pan, Z., Fu, Y., Liu, L., and Lu, B. (2022). Diffusion Model with Detail Complement for Super-Resolution of Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14194834"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.1109\/TNNLS.2020.3005234","article-title":"Deep blind hyperspectral image super-resolution","volume":"32","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_31","first-page":"1","article-title":"Unsupervised and unregistered hyperspectral image super-resolution with mutual Dirichlet-Net","volume":"60","author":"Qu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, X., Yan, H., Xie, W., Kang, L., and Tian, Y. (2020). An Improved Pulse-Coupled Neural Network Model for Pansharpening. Sensors, 20.","DOI":"10.3390\/s20102764"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"11820","DOI":"10.1109\/JSEN.2019.2948783","article-title":"Multi-modality image fusion in adaptive-parameters SPCNN based on inherent characteristics of image","volume":"20","author":"Zhang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_34","first-page":"2900","article-title":"Saliency Detection Using a Bio-inspired Spiking Neural Network Driven by Local and Global Saliency","volume":"36","author":"Bhagyashree","year":"2022","journal-title":"Appl. Artif. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"210","DOI":"10.3389\/fnins.2019.00210","article-title":"A new pulse coupled neural network (PCNN) for brain medical image fusion empowered by shuffled frog leaping algorithm","volume":"13","author":"Huang","year":"2019","journal-title":"Front. Neurosci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106141","DOI":"10.1016\/j.optlaseng.2020.106141","article-title":"Fractal dimension based parameter adaptive dual channel PCNN for multi-focus image fusion","volume":"133","author":"Panigrahy","year":"2020","journal-title":"Opt. Lasers Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"104659","DOI":"10.1016\/j.bspc.2023.104659","article-title":"Parameter adaptive unit-linking pulse coupled neural network based MRI-PET\/SPECT image fusion","volume":"83","author":"Panigrahy","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/72.761706","article-title":"PCNN models and applications","volume":"10","author":"Johnson","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"114685","DOI":"10.1016\/j.eswa.2021.114685","article-title":"Chameleon Swarm Algorithm: A Bio-inspired Optimizer for Solving Engineering Design Problems","volume":"174","author":"Braik","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ren, K., Sun, W., Meng, X., Yang, G., and Du, Q. (2020). Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?. Remote Sens., 12.","DOI":"10.3390\/rs12050882"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"8465","DOI":"10.1109\/TGRS.2020.2987955","article-title":"Hyperspectral band selection via optimal neighborhood reconstruction","volume":"58","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1080\/014311600750037499","article-title":"Smoothing Filter-based Intensity Modulation: A spectral preserve image fusion technique for improving spatial details","volume":"21","author":"Liu","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"591","DOI":"10.14358\/PERS.72.5.591","article-title":"MTF-tailored Multiscale Fusion of High-resolution MS and Pan Imagery","volume":"72","author":"Aiazzi","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3373","DOI":"10.1109\/TGRS.2014.2375320","article-title":"A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization","volume":"53","author":"Almeida","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3658","DOI":"10.1109\/TGRS.2014.2381272","article-title":"Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation","volume":"53","author":"Wei","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","first-page":"6007305","article-title":"Deep Unsupervised Blind Hyperspectral and Multispectral Data Fusion","volume":"19","author":"Li","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1007\/s12145-021-00621-6","article-title":"Hyperspectral and multispectral image fusion techniques for high resolution applications: A review","volume":"14","author":"Sara","year":"2021","journal-title":"Earth Sci. Inform."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1109\/TIP.2011.2173206","article-title":"On the mathematical properties of the structural similarity index","volume":"21","author":"Brunet","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_51","unstructured":"Tian, X., Li, K., Zhang, W., Wang, Z., and Ma, J. Interpretable Model-Driven Deep Network for Hyperspectral, Multispectral, and Panchromatic Image Fusion. IEEE Trans. Neural Netw. Learn. Syst., Available online: https:\/\/ieeexplore.ieee.org\/document\/10138912."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4118","DOI":"10.1109\/TIP.2018.2836307","article-title":"Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization","volume":"27","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","article-title":"A novel swarm intelligence optimization approach: Sparrow search algorithm","volume":"8","author":"Xue","year":"2020","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_54","first-page":"113917","article-title":"An improved grey wolf optimizer for solving engineering problems","volume":"166","author":"Taghian","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"105858","DOI":"10.1016\/j.compbiomed.2022.105858","article-title":"Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study","volume":"148","author":"Zamani","year":"2022","journal-title":"Comput. Biol. Med."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4205\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:39:53Z","timestamp":1760128793000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4205"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,26]]},"references-count":55,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15174205"],"URL":"https:\/\/doi.org\/10.3390\/rs15174205","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,26]]}}}