{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:18:57Z","timestamp":1784204337177,"version":"3.55.0"},"reference-count":57,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T00:00:00Z","timestamp":1623801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008996","name":"Capital Normal University","doi-asserted-by":"publisher","award":["21220030003"],"award-info":[{"award-number":["21220030003"]}],"id":[{"id":"10.13039\/100008996","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>ZY-1 02D is China\u2019s first civil hyperspectral (HS) operational satellite, developed independently and successfully launched in 2019. It can collect HS data with a spatial resolution of 30 m, 166 spectral bands, a spectral range of 400~2500 nm, and a swath width of 60 km. Its competitive advantages over other on-orbit or planned satellites are its high spectral resolution and large swath width. Unfortunately, the relatively low spatial resolution may limit its applications. As a result, fusing ZY-1 02D HS data with high-spatial-resolution multispectral (MS) data is required to improve spatial resolution while maintaining spectral fidelity. This paper conducted a comprehensive evaluation study on the fusion of ZY-1 02D HS data with ZY-1 02D MS data (10-m spatial resolution), based on visual interpretation and quantitative metrics. Datasets from Hebei, China, were used in this experiment, and the performances of six common data fusion methods, namely Gram-Schmidt (GS), High Pass Filter (HPF), Nearest-Neighbor Diffusion (NND), Modified Intensity-Hue-Saturation (IHS), Wavelet Transform (Wavelet), and Color Normalized Sharping (Brovey), were compared. The experimental results show that: (1) HPF and GS methods are better suited for the fusion of ZY-1 02D HS Data and MS Data, (2) IHS and Brovey methods can well improve the spatial resolution of ZY-1 02D HS data but introduce spectral distortion, and (3) Wavelet and NND results have high spectral fidelity but poor spatial detail representation. The findings of this study could serve as a good reference for the practical application of ZY-1 02D HS data fusion.<\/jats:p>","DOI":"10.3390\/rs13122354","type":"journal-article","created":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T21:58:32Z","timestamp":1623880712000},"page":"2354","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Fusion of China ZY-1 02D Hyperspectral Data and Multispectral Data: Which Methods Should Be Used?"],"prefix":"10.3390","volume":"13","author":[{"given":"Han","family":"Lu","sequence":"first","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"College of Geospatial Information Science and Technology, Capital Normal University, Beijing 100048, China"},{"name":"Key Laboratory of 3D Information Acquisition and Application, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danyu","family":"Qiao","sequence":"additional","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"College of Geospatial Information Science and Technology, Capital Normal University, Beijing 100048, China"},{"name":"Key Laboratory of 3D Information Acquisition and Application, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongxin","family":"Li","sequence":"additional","affiliation":[{"name":"Logistics Support Department, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"College of Geospatial Information Science and Technology, Capital Normal University, Beijing 100048, China"},{"name":"Key Laboratory of 3D Information Acquisition and Application, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4574-7381","authenticated-orcid":false,"given":"Lei","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"College of Geospatial Information Science and Technology, Capital Normal University, Beijing 100048, China"},{"name":"Key Laboratory of 3D Information Acquisition and Application, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,16]]},"reference":[{"key":"ref_1","first-page":"1050","article-title":"Progress and future of remote sensing data fusion","volume":"20","author":"Zhang","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xie, Q., Zhou, M., Zhao, Q., Meng, D., Zuo, W., and Xu, Z. (2019, January 15\u201320). Multispectral and Hyperspectral Image Fusion by MS\/HS Fusion Net. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00168"},{"key":"ref_3","first-page":"4995","article-title":"Multifeature-Based Discriminative Label Consistent K-SVD for Hyperspectral Image Classification","volume":"12","author":"Ma","year":"2019","journal-title":"IEEE J. Stars."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Qu, Y., Qi, H., Ayhan, B., Kwan, C., and Kidd, R. (2017, January 23\u201328). DOES multispectral\/hyperspectral pansharpening improve the performance of anomaly detection?. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128408"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2016.2616355","article-title":"Hyperspectral Image Classification Using Deep Pixel-Pair Features","volume":"55","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/TCI.2017.2692645","article-title":"Robust Fusion of Multi-Band Images with Different Spatial and Spectral Resolutions for Change Detection","volume":"3","author":"Ferraris","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1109\/JPROC.2015.2449668","article-title":"Multimodal classification of remote sensing images: A review and future directions","volume":"103","author":"Tuia","year":"2015","journal-title":"Proc. IEEE"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2012.2197589","article-title":"Advances in Spectral-Spatial Classification of Hyperspectral Images","volume":"101","author":"Fauvel","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.inffus.2016.03.003","article-title":"A review of remote sensing image fusion methods","volume":"32","author":"Ghassemian","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_10","first-page":"22","article-title":"Image Fusion in Remote Sensing Applications: A Review","volume":"120","author":"Pandit","year":"2015","journal-title":"Int. J. Comput. Appl."},{"key":"ref_11","first-page":"1492","article-title":"Research Status and Prospect of Spatiotemporal Fusion of Multi-source Satellite Remote Sensing Imagery","volume":"46","author":"Huang","year":"2017","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_12","first-page":"68","article-title":"Remote Sensing Image Fusion Using Wavelet Packet Transform","volume":"9","author":"Wang","year":"2002","journal-title":"J. Image Graph."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTARS.2019.2910990","article-title":"Pyramid Fully Convolutional Network for Hyperspectral and Multispectral Image Fusion","volume":"12","author":"Zhou","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_14","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_15","first-page":"5","article-title":"Comparison of diffirent fusion methods and their performance evaluation to high spatial resolution remote sensing data of GF","volume":"5","author":"Shao","year":"2019","journal-title":"Bulletin Surv. Mapp."},{"key":"ref_16","first-page":"121","article-title":"Comparison and analysis is of pixel level image fusion algorithms application to ALOS data","volume":"33","author":"Wang","year":"2008","journal-title":"Sci. Surv. Mapp."},{"key":"ref_17","first-page":"100","article-title":"Satellite image maps of Warsaw in the scale 1:25,000","volume":"4","author":"Kaczynski","year":"1995","journal-title":"Earsel Adv. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3230","DOI":"10.1109\/TGRS.2007.901007","article-title":"Improving Component Substitution Pansharpening Through Multivariate Regression of MS +Pan Data","volume":"45","author":"Aiazzi","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/LGRS.2004.834804","article-title":"A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery","volume":"1","author":"Tu","year":"2004","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Metwalli, M.R., Nasr, A.H., Allah, O.S.F., and El-Rabaie, S. (2009, January 14\u201316). Image fusion based on principal component analysis and high-pass filter. Proceedings of the 2009 International Conference on Computer Engineering & Systems, Cairo, Egypt.","DOI":"10.1109\/ICCES.2009.5383308"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"13107","DOI":"10.1117\/1.OE.53.1.013107","article-title":"Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images","volume":"53","author":"Sun","year":"2013","journal-title":"Opt. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1080\/014311698215973","article-title":"A wavelet transform method to merge Landsat TM and SPOT panchromatic data","volume":"19","author":"Zhou","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1109\/TGRS.2008.916211","article-title":"An Efficient Pan-Sharpening Method via a Combined Adaptive PCA Approach and Contourlets","volume":"46","author":"Shah","year":"2008","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"93","DOI":"10.2747\/1548-1603.44.2.93","article-title":"Image Fusion Using the Ehlers Spectral Characteristics Preserving Algorithm","volume":"44","author":"Klonus","year":"2007","journal-title":"GISci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"116201","DOI":"10.1117\/1.2124871","article-title":"Adjustable intensity-hue-saturation and Brovey transform fusion technique for IKONOS\/QuickBird imagery","volume":"44","author":"Tu","year":"2005","journal-title":"Opt. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Huang, W., Liu, M., Zhang, H., and Zou, B. (2010, January 18\u201320). Fusion of satellite images in urban area: Assessing the quality of resulting images. Proceedings of the 2010 18th International Conference on Geoinformatics, Beijing, China.","DOI":"10.1109\/GEOINFORMATICS.2010.5568105"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Teo, T., and Fu, Y. (2021). Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of \u201cSuper Resolution-Then-Blend\u201d and \u201cBlend-Then-Super Resolution\u201d Approaches. Remote Sens., 13.","DOI":"10.3390\/rs13040606"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2532","DOI":"10.1080\/01431161.2011.616552","article-title":"Comparison of multisource image fusion methods and land cover classification","volume":"33","author":"Amarsaikhan","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2309","DOI":"10.1080\/01431160600606890","article-title":"A comparison study on fusion methods using evaluation indicators","volume":"28","author":"Karathanassi","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","first-page":"6","article-title":"Research on Fusion of Mapping Satellite-1 Imagery and Its Evaluation","volume":"430","author":"Huang","year":"2013","journal-title":"Bulletin Surv. Mapp."},{"key":"ref_31","first-page":"108","article-title":"Research on fusion of GF-2 imagery and quality evaluation","volume":"28","author":"Sun","year":"2016","journal-title":"Remote Sens. Land Resour."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1007\/s13131-019-1382-x","article-title":"Performances of conventional fusion methods evaluated for inland water body observation using GF-1 image","volume":"38","author":"Du","year":"2019","journal-title":"Acta Oceanol. Sin."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1109\/LGRS.2013.2278551","article-title":"Quality Assessment of Panchromatic and Multispectral Image Fusion for the ZY-3 Satellite: From an Information Extraction Perspective","volume":"11","author":"Huang","year":"2014","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","unstructured":"Ghimire, P., Lei, D., and Juan, N. (2020). Effect of Image Fusion on Vegetation Index Quality\u2014A Comparative Study from Gaofen-1, Gaofen-2, Gaofen-4, Landsat-8 OLI and MODIS Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12101550"},{"key":"ref_36","first-page":"157","article-title":"Comparison on fusion algorithms of ZY-3 panchromatic and multi-spectral images","volume":"30","author":"Li","year":"2014","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zheng, Y., and Han, X. (2021). Unsupervised Multispectral and Hyperspectral Image Fusion with Deep Spatial and Spectral Priors, Springer International Publishing.","DOI":"10.1007\/978-3-030-69756-3_3"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1109\/TNNLS.2020.2980398","article-title":"Regularizing Hyperspectral and Multispectral Image Fusion by CNN Denoiser","volume":"32","author":"Dian","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1109\/JSTARS.2018.2805923","article-title":"Remote Sensing Image Fusion With Deep Convolutional Neural Network","volume":"11","author":"Shao","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kwan, C., Choi, J.H., Chan, S., Jin, Z., and Budavari, B. (2017, January 5\u20139). Resolution enhancement for hyperspectral images: A super-resolution and fusion approach. Proceedings of the ICASSP 2017\u20132017 IEEE International Conference on Acoustics, Speech and Signal, New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7953344"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MGRS.2015.2440094","article-title":"Hyperspectral Pansharpening: A Review","volume":"3","author":"Loncan","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_42","first-page":"1453","article-title":"Spectral or spatial quality for fused satellite imagery? A trade-off solution using the wavelet\u00e0 trous algorithm","volume":"27","author":"Gonzalo","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jel\u00e9nek, J., Kopa\u010dkov\u00e1, V., Kouck\u00e1, L., and Mi\u0161urec, J. (2016). Testing a Modified PCA-Based Sharpening Approach for Image Fusion. Remote Sens., 8.","DOI":"10.3390\/rs8100794"},{"key":"ref_44","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_45","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. Rem. Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1109\/36.763274","article-title":"Multiresolution-based image fusion with additive wavelet decomposition","volume":"37","author":"Nunez","year":"1999","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_47","first-page":"35","article-title":"Quality assessment on image fusion methods for GF-2 data","volume":"42","author":"Chen","year":"2017","journal-title":"Sci. Surv. Mapp."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1109\/TGRS.2006.869923","article-title":"A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter","volume":"44","author":"Choi","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","first-page":"343","article-title":"Improved Brovey method for multi-sensor image fusion","volume":"16","author":"Zhou","year":"2012","journal-title":"J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1109\/TGRS.2010.2087029","article-title":"An IHS-Based Fusion for Color Distortion Reduction and Vegetation Enhancement in IKONOS Imagery","volume":"49","author":"Taleb","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pei, W., Wang, G., and Yu, X. (2012). Performance evaluation of different references based image fusion quality metrics for quality assessment of remote sensing Image fusion. Geosci. Remote Sens. Symp., 2280\u20132283.","DOI":"10.1109\/IGARSS.2012.6351040"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"88385","DOI":"10.1109\/ACCESS.2020.2993607","article-title":"Edge Information Based Image Fusion Metrics Using Fractional Order Differentiation and Sigmoidal Functions","volume":"8","author":"Sengupta","year":"2020","journal-title":"IEEE Access"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.neucom.2016.06.091","article-title":"Object-based quality evaluation procedure for fused remote sensing imagery","volume":"255","author":"Marcello","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Esparrag\u00f3n, D. (2014). Evaluation of the performance of spatial assessments of pansharpened images. Geosci. Remote Sens. Symp., 1619\u20131622.","DOI":"10.1109\/IGARSS.2014.6946757"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lu, H., Fan, T., Ghimire, P., and Deng, L. (2020). Experimental Evaluation and Consistency Comparison of UAV Multispectral Minisensors. Remote Sens., 12.","DOI":"10.3390\/rs12162542"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1007\/s11045-019-00684-1","article-title":"Multi-scale RoIs selection for classifying multi-spectral images","volume":"31","author":"Seal","year":"2020","journal-title":"Multidimens. Syst. Sign. Process"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wang, B., Jaewan, C., Seokeun, C., Soungki, L., Wu, P., and Yan, G. (2017). Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images. Remote Sens., 9.","DOI":"10.3390\/rs9080804"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2354\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:17:13Z","timestamp":1760163433000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2354"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,16]]},"references-count":57,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13122354"],"URL":"https:\/\/doi.org\/10.3390\/rs13122354","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,16]]}}}