{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T06:24:53Z","timestamp":1769063093389,"version":"3.49.0"},"reference-count":71,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"International Research Center of Big Data for Sustainable Development Goals","award":["CBAS2022RP04"],"award-info":[{"award-number":["CBAS2022RP04"]}]},{"name":"International Research Center of Big Data for Sustainable Development Goals","award":["2022YFC3800701"],"award-info":[{"award-number":["2022YFC3800701"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["CBAS2022RP04"],"award-info":[{"award-number":["CBAS2022RP04"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3800701"],"award-info":[{"award-number":["2022YFC3800701"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Sustainable Development Science Satellite 1 (SDGSAT-1) satellite, launched in November 2021, is dedicated to providing data detailing the \u201ctraces of human activities\u201d for the implementation of the United Union\u2019s 2030 Agenda for Sustainable Development and global scientific research. The glimmer imager (GI) that is equipped on SDGSAT-1 can provide nighttime light (NL) data with a 10 m panchromatic (PAN) band and red, green, and blue (RGB) bands of 40 m resolution, which can be used for a wide range of applications, such as in urban expansion, population studies of cities, and economics of cities, as well as nighttime aerosol thickness monitoring. The 10 m PAN band can be fused with the 40 m RGB bands to obtain a 10 m RGB NL image, which can be used to identify the intensity and type of night lights and the spatial distribution of road networks and to improve the monitoring accuracy of sustainable development goal (SDG) indicators related to city developments. Existing remote sensing image fusion algorithms are mainly developed for daytime optical remote sensing images. Compared with daytime optical remote sensing images, NL images are characterized by a large amount of dark (low-value) pixels and high background noises. To investigate whether daytime optical image fusion algorithms are suitable for the fusion of GI NL images and which image fusion algorithms are the best choice for GI images, this study conducted a comprehensive evaluation of thirteen state-of-the-art pansharpening algorithms in terms of quantitative indicators and visual inspection using four GI NL datasets. The results showed that PanNet, GLP_HPM, GSA, and HR outperformed the other methods and provided stable performances among the four datasets. Specifically, PanNet offered UIQI values ranging from 0.907 to 0.952 for the four datasets, whereas GSA, HR, and GLP_HPM provided UIQI values ranging from 0.770 to 0.856. The three methods based on convolutional neural networks achieved more robust and better visual effects than the methods using multiresolution analysis at the original scale. According to the experimental results, PanNet shows great potential in the fusion of SDGSAT-1 GI imagery due to its robust performance and relatively short training time. The quality metrics generated at the degraded scale were highly consistent with visual inspection, but those used at the original scale were inconsistent with visual inspection.<\/jats:p>","DOI":"10.3390\/rs16020245","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T07:59:20Z","timestamp":1704700760000},"page":"245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Comprehensive Assessment of the Pansharpening of the Nighttime Light Imagery of the Glimmer Imager of the Sustainable Development Science Satellite 1"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3565-1773","authenticated-orcid":false,"given":"Hui","family":"Li","sequence":"first","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Hainan Key Laboratory of Earth Observation, Hainan Aerospace Information Research Institute, Wenchang 571399, China"}]},{"given":"Linhai","family":"Jing","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Changyong","family":"Dou","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2031-1725","authenticated-orcid":false,"given":"Haifeng","family":"Ding","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"ref_1","unstructured":"(2022, June 05). SDGeHandbook. Available online: https:\/\/unstats.un.org\/wiki\/display\/SDGeHandbook?preview=\/34505092\/106497383\/SDGeHandbook-111121-2121-805.pdf."},{"key":"ref_2","unstructured":"(2022, June 05). Indicators List. Available online: https:\/\/unstats.un.org\/sdgs\/indicators\/indicators-list\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111443","DOI":"10.1016\/j.rse.2019.111443","article-title":"Remote sensing of night lights: A review and an outlook for the future","volume":"237","author":"Levin","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2014.03.004","article-title":"A cluster-based method to map urban area from DMSP\/OLS nightlights","volume":"147","author":"Zhou","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, X., de Sherbinin, A., and Zhan, Y. (2019). Mapping urban extent at large spatial scales using machine learning methods with VIIRS nighttime light and MODIS daytime NDVI data. Remote Sens., 11.","DOI":"10.3390\/rs11101247"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, G., Guo, X., Li, D., and Jiang, B. (2019). Evaluating the potential of LJ1-01 nighttime light data for modeling socio-economic parameters. Sensors, 19.","DOI":"10.3390\/s19061465"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, H., Luo, N., and Hu, C. (2020). Detection of county economic development using LJ1-01 nighttime light imagery: A comparison with NPP-VIIRS data. Sensors, 20.","DOI":"10.3390\/s20226633"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.isprsjprs.2019.03.011","article-title":"A spatially structured adaptive two-stage model for retrieving ground-level PM2.5 concentrations from VIIRS AOD in china","volume":"151","author":"Yao","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3618","DOI":"10.1109\/JSTARS.2020.3002671","article-title":"Evaluation of LJ1-01 nighttime light imagery for estimating monthly PM2.5 concentration: A comparison with NPP-VIIRS nighttime light data","volume":"13","author":"Zhang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhao, M., Zhou, Y., Li, X., Cao, W., He, C., Yu, B., Li, X., Elvidge, C.D., Cheng, W., and Zhou, C. (2019). Applications of satellite remote sensing of nighttime light observations: Advances, challenges, and perspectives. Remote Sens., 11.","DOI":"10.3390\/rs11171971"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3076011","article-title":"Coloring panchromatic nighttime satellite images: Comparing the performance of several machine learning methods","volume":"60","author":"Rybnikova","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.rse.2018.06.016","article-title":"A new source of multi-spectral high spatial resolution night-time light imagery\u2014jl1-3b","volume":"215","author":"Zheng","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2014.03.019","article-title":"A new source for high spatial resolution night time images\u2014The EROS-B commercial satellite","volume":"149","author":"Levin","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11175","DOI":"10.1073\/pnas.1708574114","article-title":"High-intensity urban light installation dramatically alters nocturnal bird migration","volume":"114","author":"Horton","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_15","unstructured":"(2022, July 09). User Guide of SDGSAT-1 (Released on July 2022). Available online: http:\/\/124.16.184.48:6008\/downresouce."},{"key":"ref_16","first-page":"459","article-title":"The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectralimage data","volume":"56","author":"Carper","year":"1990","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_17","first-page":"561","article-title":"A generalized component substitution technique for spatial enhacement of multispectral images using a higher resolution dataset","volume":"58","author":"Shettigara","year":"1992","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_18","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_19","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_20","unstructured":"Bochenek, Z. (2007). New Developments and Challenges in Remote Sensing, Millpress."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1080\/01431160802549260","article-title":"Two improvement schemes of pan modulation fusion methods for spectral distortion minimization","volume":"30","author":"Jing","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1080\/01431160903527405","article-title":"An image fusion method for misaligned panchromatic and multispectral data","volume":"32","author":"Jing","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Li, H., Jing, L., Tang, Y., and Ding, H. (2018). An improved pansharpening method for misaligned panchromatic and multispectral data. Sensors, 18.","DOI":"10.3390\/s18020557"},{"key":"ref_25","first-page":"295","article-title":"Comparison of three different methods to merge multiresolution and multispectral data","volume":"57","author":"Chavez","year":"1991","journal-title":"Photogramm. Enginnering Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.1109\/78.157290","article-title":"The discrete wavelet transform: Wedding the a trous and Mallat algorithms","volume":"40","author":"Shensa","year":"1992","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_27","first-page":"1183","article-title":"Multispectral fusion of multisensor image data by the generalized Laplacian pyramid","volume":"2","author":"Aiazzi","year":"1999","journal-title":"IEEE Int. Geosci. Remote Sens. Symp."},{"key":"ref_28","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 Sens."},{"key":"ref_29","first-page":"1","article-title":"Generalised Laplacian pyramid-based fusion of MS + P image data with spectral distortion minimisation","volume":"34","author":"Aiazzi","year":"2002","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1109\/TGRS.2005.856106","article-title":"Introduction of sensor spectral response into image fusion methods: Application to wavelet-based methods","volume":"43","author":"Otazu","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","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":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.isprsjprs.2007.05.009","article-title":"Wavelet based image fusion techniques\u2014An introduction, review and comparison","volume":"62","author":"Amolins","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1080\/01431160701313826","article-title":"Comparison and improvement of wavelet-based image fusion","volume":"29","author":"Hong","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","first-page":"12","article-title":"Advantages of laplacian pyramids over \u201c\u00e0 trous\u201d wavelet transforms for pansharpening of multispectral images","volume":"853704","author":"Bruzzone","year":"2012","journal-title":"Proc. SPIE Image Signal Process. Remote Sens. XVIII"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2015.02.015","article-title":"Remote sensing image fusion via wavelet transform and sparse representation","volume":"104","author":"Cheng","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"83565","DOI":"10.1117\/1.JRS.8.083565","article-title":"Variational model-based very high spatial resolution remote sensing image fusion","volume":"8","author":"Cao","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1080\/2150704X.2015.1041170","article-title":"Parameter selection for variational pan-sharpening by using evolutionary algorithm","volume":"6","author":"Xiao","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1080\/01431161.2015.1014973","article-title":"Pan-sharpening of multi-spectral images using a new variational model","volume":"36","author":"Zhang","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5276","DOI":"10.1109\/JSTARS.2016.2537925","article-title":"A new geometry enforcing variational model for pan-sharpening","volume":"9","author":"Liu","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2016.12.013","article-title":"A survey of pansharpening methods with a new band-decoupled variational model","volume":"125","author":"Duran","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Masi, G., Cozzolino, D., Verdoliva, L., and Scarpa, G. (2016). Pansharpening by convolutional neural networks. Remote Sens., 8.","DOI":"10.3390\/rs8070594"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.1109\/LGRS.2017.2736020","article-title":"Boosting the accuracy of multispectral image pansharpening by learning a deep residual network","volume":"14","author":"Wei","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yang, J., Fu, X., Hu, Y., Huang, Y., Ding, X., and Paisley, J. (2017, January 24\u201327). PanNet: A deep network architecture for pan-sharpening. Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.193"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.inffus.2017.10.007","article-title":"Deep learning for pixel-level image fusion: Recent advances and future prospects","volume":"42","author":"Liu","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5443","DOI":"10.1109\/TGRS.2018.2817393","article-title":"Target-adaptive CNN-based pansharpening","volume":"56","author":"Scarpa","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, Z., and Cheng, C. (2019). A CNN-based pan-sharpening method for integrating panchromatic and multispectral images using Landsat 8. Remote Sens., 11.","DOI":"10.3390\/rs11222606"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.isprsjprs.2020.03.006","article-title":"A differential information residual convolutional neural network for pansharpening","volume":"163","author":"Jiang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Vitale, S., and Scarpa, G. (2020). A detail-preserving cross-scale learning strategy for CNN-based pansharpening. Remote Sens., 12.","DOI":"10.3390\/rs12030348"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3163887","article-title":"Pansharpening by convolutional neural networks in the full resolution framework","volume":"60","author":"Ciotola","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Scarpa, G., and Ciotola, M. (2022). Full-resolution quality assessment for pansharpening. Remote Sens., 14.","DOI":"10.3390\/rs14081808"},{"key":"ref_52","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_53","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_54","doi-asserted-by":"crossref","first-page":"8336","DOI":"10.1080\/01431161.2013.838706","article-title":"Assessment of pan-sharpened very high-resolution worldview-2 images","volume":"34","author":"Ghosh","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.polar.2012.12.002","article-title":"A spectral index ratio-based antarctic land-cover mapping using hyperspatial 8-band worldview-2 imagery","volume":"7","author":"Jawak","year":"2013","journal-title":"Polar Sci."},{"key":"ref_56","first-page":"673","article-title":"Pan-sharpening worldview-2 IHS, brovey and zhang methods in comparison","volume":"8","author":"Maglione","year":"2016","journal-title":"Int. J. Eng. Technol."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, H., Jing, L., and Tang, Y. (2017). Assessment of pansharpening methods applied to Worldview-2 imagery fusion. Sensors, 17.","DOI":"10.3390\/s17010089"},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TGRS.2014.2361734","article-title":"A critical comparison among pansharpening algorithms","volume":"33","author":"Vivone","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.inffus.2004.06.008","article-title":"Interband structure modeling for pan-sharpening of very high-resolution multispectral images","volume":"6","author":"Garzelli","year":"2005","journal-title":"Inf. Fusion"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1109\/TGRS.2009.2028613","article-title":"Fast and efficient panchromatic sharpening","volume":"48","author":"Lee","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/S0924-2716(03)00013-3","article-title":"Image fusion\u2014The arsis concept and some successful implementation schemes","volume":"58","author":"Ranchin","year":"2003","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_64","unstructured":"Yuhas, R., Goetz, A., and Boardman, J. (1992, January 1). Discrimination among semi-arid landscape endmembers using the spectral angle mapper (sam) algorithm. Proceedings of the Summaries of the Third Annual JPL Airborne Geoscience Workshop, Pasadena, CA, USA."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/LGRS.2004.836784","article-title":"A global quality measurement of pan-sharpened multispectral imagery","volume":"1","author":"Alparone","year":"2004","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_66","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_67","first-page":"600","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.inffus.2006.09.001","article-title":"A novel similarity based quality metric for image fusion","volume":"9","author":"Yang","year":"2008","journal-title":"Inf. Fusion"},{"key":"ref_69","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_70","doi-asserted-by":"crossref","first-page":"3880","DOI":"10.1109\/TGRS.2009.2029094","article-title":"Pansharpening quality assessment using the modulation transfer functions of instruments","volume":"47","author":"Khan","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1109\/MGRS.2022.3170092","article-title":"Full-resolution quality assessment of pansharpening: Theoretical and hands-on approaches","volume":"10","author":"Arienzo","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/245\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:42:18Z","timestamp":1760103738000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/245"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,8]]},"references-count":71,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020245"],"URL":"https:\/\/doi.org\/10.3390\/rs16020245","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,8]]}}}