{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:54:10Z","timestamp":1771955650253,"version":"3.50.1"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1016\/j.neunet.2023.10.058","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T14:35:32Z","timestamp":1699454132000},"page":"698-712","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":16,"special_numbering":"C","title":["A benchmarking protocol for SAR colorization: From regression to deep learning approaches"],"prefix":"10.1016","volume":"169","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6136-5594","authenticated-orcid":false,"given":"Kangqing","family":"Shen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9542-0638","authenticated-orcid":false,"given":"Gemine","family":"Vivone","sequence":"additional","affiliation":[]},{"given":"Xiaoyuan","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6111-152X","authenticated-orcid":false,"given":"Simone","family":"Lolli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0575-2362","authenticated-orcid":false,"given":"Michael","family":"Schmitt","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.neunet.2023.10.058_b1","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 Geoscience and Remote Sensing Letters"},{"key":"10.1016\/j.neunet.2023.10.058_b2","first-page":"379","article-title":"Influential observations, high leverage points, and outliers in linear regression","author":"Chatterjee","year":"1986","journal-title":"Statistical Science"},{"key":"10.1016\/j.neunet.2023.10.058_b3","series-title":"2020 IEEE\/CVF conference on computer vision and pattern recognition","first-page":"8165","article-title":"Reusing discriminators for encoding: Towards unsupervised image-to-image translation","author":"Chen","year":"2020"},{"issue":"5","key":"10.1016\/j.neunet.2023.10.058_b4","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.isprsjprs.2006.05.001","article-title":"Additive integration of SAR features into multispectral SPOT images by means of the \u00e0 trous wavelet decomposition","volume":"60","author":"Chibani","year":"2006","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"10.1016\/j.neunet.2023.10.058_b5","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.isprsjprs.2020.11.001","article-title":"A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery","volume":"171","author":"Dadrass Javan","year":"2021","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"10.1016\/j.neunet.2023.10.058_b6","series-title":"2008 congress on image and signal processing, Vol. 4","first-page":"697","article-title":"Colorization for polarimetric SAR image based on scattering mechanisms","author":"Deng","year":"2008"},{"key":"10.1016\/j.neunet.2023.10.058_b7","series-title":"2017 IEEE conference on computer vision and pattern recognition","first-page":"2877","article-title":"Learning diverse image colorization","author":"Deshpande","year":"2017"},{"key":"10.1016\/j.neunet.2023.10.058_b8","series-title":"Applied regression analysis, vol. 326","author":"Draper","year":"1998"},{"issue":"7","key":"10.1016\/j.neunet.2023.10.058_b9","doi-asserted-by":"crossref","first-page":"5866","DOI":"10.1109\/TGRS.2020.3024744","article-title":"SEN12MS-CR: Multi-sensor data fusion for cloud removal in global and all-season sentinel-2 imagery","volume":"59","author":"Ebel","year":"2021","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"11","key":"10.1016\/j.neunet.2023.10.058_b10","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Communications of the ACM"},{"key":"10.1016\/j.neunet.2023.10.058_b11","doi-asserted-by":"crossref","unstructured":"He, K., & Sun, J. (2015). Convolutional neural networks at constrained time cost. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5353\u20135360).","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"10.1016\/j.neunet.2023.10.058_b12","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1125\u20131134).","DOI":"10.1109\/CVPR.2017.632"},{"issue":"2","key":"10.1016\/j.neunet.2023.10.058_b13","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1109\/LGRS.2020.2969891","article-title":"SAR image colorization using multidomain cycle-consistency generative adversarial network","volume":"18","author":"Ji","year":"2021","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"21","key":"10.1016\/j.neunet.2023.10.058_b14","doi-asserted-by":"crossref","first-page":"4274","DOI":"10.3390\/rs13214274","article-title":"A fusion method of optical image and SAR image based on dense-UGAN and gram-Schmidt transformation","volume":"13","author":"Kong","year":"2021","journal-title":"Remote Sensing"},{"issue":"6_3","key":"10.1016\/j.neunet.2023.10.058_b15","first-page":"1415","article-title":"The method for colorizing SAR images of Kompsat-5 using cycle GAN with multi-scale discriminators","volume":"34","author":"Ku","year":"2018","journal-title":"Korean Journal of Remote Sensing"},{"key":"10.1016\/j.neunet.2023.10.058_b16","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.inffus.2020.01.003","article-title":"Pixel level fusion techniques for SAR and optical images: A review","volume":"59","author":"Kulkarni","year":"2020","journal-title":"Information Fusion"},{"issue":"10","key":"10.1016\/j.neunet.2023.10.058_b17","doi-asserted-by":"crossref","first-page":"776","DOI":"10.5515\/KJKIEES.2022.33.10.776","article-title":"Labeling dataset based colorization of SAR images using cycle GAN","volume":"33","author":"Lee","year":"2022","journal-title":"The Journal of Korean Institute of Electromagnetic Engineering and Science"},{"key":"10.1016\/j.neunet.2023.10.058_b18","doi-asserted-by":"crossref","unstructured":"Lee, J. H., Kim, K., & Kim, J. H. (2021). Design of CycleGAN Model for SAR Image Colorization. In 2021 IEEE VTS 17th Asia Pacific wireless communications symposium (pp. 1\u20135).","DOI":"10.1109\/APWCS50173.2021.9548749"},{"key":"10.1016\/j.neunet.2023.10.058_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.jag.2022.102926","article-title":"Deep learning in multimodal remote sensing data fusion: A comprehensive review","volume":"112","author":"Li","year":"2022","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"key":"10.1016\/j.neunet.2023.10.058_b20","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-70139-4","article-title":"Unsupervised image-to-image translation networks","volume":"30","author":"Liu","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"2","key":"10.1016\/j.neunet.2023.10.058_b21","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/MGRS.2022.3165967","article-title":"Remote sensing data fusion with generative adversarial networks: State-of-the-art methods and future research directions","volume":"10","author":"Liu","year":"2022","journal-title":"IEEE Geoscience and Remote Sensing Magazine"},{"issue":"12","key":"10.1016\/j.neunet.2023.10.058_b22","doi-asserted-by":"crossref","first-page":"10227","DOI":"10.1109\/TGRS.2020.3042974","article-title":"PSGAN: A generative adversarial network for remote sensing image pan-sharpening","volume":"59","author":"Liu","year":"2021","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"12","key":"10.1016\/j.neunet.2023.10.058_b23","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1109\/LGRS.2017.2761021","article-title":"Haze correction for contrast-based multispectral pansharpening","volume":"14","author":"Lolli","year":"2017","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"1","key":"10.1016\/j.neunet.2023.10.058_b24","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1109\/36.823941","article-title":"Experimental validation of surface scattering and emission models","volume":"38","author":"Macelloni","year":"2000","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.neunet.2023.10.058_b25","series-title":"Conditional generative adversarial nets","author":"Mirza","year":"2014"},{"issue":"4","key":"10.1016\/j.neunet.2023.10.058_b26","doi-asserted-by":"crossref","first-page":"3486","DOI":"10.1109\/TGRS.2020.3010441","article-title":"PanColorGAN:Rethinking CNN-based pansharpening: Guided colorization of panchromatic images via GANs","volume":"59","author":"Ozcelik","year":"2021","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.neunet.2023.10.058_b27","series-title":"Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.neunet.2023.10.058_b28","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.5194\/isprs-archives-XLII-2-1045-2018","article-title":"Colorizing sentinel-1 sar images using a variational autoencoder conditioned on sentinel-2 imagery","volume":"42","author":"Schmitt","year":"2018","journal-title":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences"},{"key":"10.1016\/j.neunet.2023.10.058_b29","doi-asserted-by":"crossref","first-page":"141","DOI":"10.5194\/isprs-annals-IV-1-141-2018","article-title":"SEN12: The Sen1-2 dataset for deep learning in SAR-optical data fusion","volume":"IV-1","author":"Schmitt","year":"2018","journal-title":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences"},{"key":"10.1016\/j.neunet.2023.10.058_b30","doi-asserted-by":"crossref","unstructured":"Schmitt, M., Tupin, F., & Zhu, X. X. (2017). Fusion of SAR and Optical Remote Sensing Data: Challenges and Recent Trends. In 2017 IEEE international geoscience and remote sensing symposium (pp. 5458\u20135461). [ISSN: 2153-7003].","DOI":"10.1109\/IGARSS.2017.8128239"},{"issue":"4","key":"10.1016\/j.neunet.2023.10.058_b31","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 Geoscience and Remote sensing letters"},{"key":"10.1016\/j.neunet.2023.10.058_b32","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":"Information Fusion"},{"issue":"5","key":"10.1016\/j.neunet.2023.10.058_b33","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 Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.neunet.2023.10.058_b34","doi-asserted-by":"crossref","first-page":"6102","DOI":"10.1109\/JSTARS.2021.3086877","article-title":"A benchmarking protocol for pansharpening: Dataset, preprocessing, and quality assessment","volume":"14","author":"Vivone","year":"2021","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"1","key":"10.1016\/j.neunet.2023.10.058_b35","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":"2021","journal-title":"IEEE Geoscience and Remote Sensing Magazine"},{"issue":"3","key":"10.1016\/j.neunet.2023.10.058_b36","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 Processing Letters"},{"issue":"4","key":"10.1016\/j.neunet.2023.10.058_b37","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 Transactions on Image Processing"},{"key":"10.1016\/j.neunet.2023.10.058_b38","first-page":"1","article-title":"Hybrid cGAN: Coupling global and local features for SAR-to-optical image translation","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.neunet.2023.10.058_b39","doi-asserted-by":"crossref","unstructured":"Wang, X., Xie, L., Dong, C., & Shan, Y. (2021). Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. In International conference on computer vision workshops.","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"10.1016\/j.neunet.2023.10.058_b40","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., et al. (2018). ESRGAN: Enhanced super-resolution generative adversarial networks. In The European conference on computer vision workshops.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"10.1016\/j.neunet.2023.10.058_b41","first-page":"1","article-title":"FG-GAN: A fine-grained generative adversarial network for unsupervised SAR-to-optical image translation","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.neunet.2023.10.058_b42","first-page":"1","article-title":"An unsupervised SAR and optical image fusion network based on structure-texture decomposition","volume":"19","author":"Ye","year":"2022","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"10.1016\/j.neunet.2023.10.058_b43","unstructured":"Yuhas, R. H., Goetz, A. F., & Boardman, J. W. (1992). Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm. In JPL, summaries of the third annual JPL airborne geoscience workshop. volume 1: AVIRIS workshop."},{"key":"10.1016\/j.neunet.2023.10.058_b44","series-title":"Computer vision\u2013ECCV 2016: 14th European conference, Amsterdam, the Netherlands, October 11-14, 2016, proceedings, part III 14","first-page":"649","article-title":"Colorful image colorization","author":"Zhang","year":"2016"},{"issue":"4","key":"10.1016\/j.neunet.2023.10.058_b45","doi-asserted-by":"crossref","first-page":"870","DOI":"10.3390\/rs14040870","article-title":"Multispectral and SAR image fusion based on Laplacian pyramid and sparse representation","volume":"14","author":"Zhang","year":"2022","journal-title":"Remote Sensing"},{"key":"10.1016\/j.neunet.2023.10.058_b46","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.inffus.2021.06.008","article-title":"Image fusion meets deep learning: A survey and perspective","volume":"76","author":"Zhang","year":"2021","journal-title":"Information Fusion"},{"key":"10.1016\/j.neunet.2023.10.058_b47","doi-asserted-by":"crossref","first-page":"6316","DOI":"10.1109\/JSTARS.2021.3090252","article-title":"PGMAN: An unsupervised generative multiadversarial network for pansharpening","volume":"14","author":"Zhou","year":"2021","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"10.1016\/j.neunet.2023.10.058_b48","first-page":"1","article-title":"UCGAN: Unsupervised cycle-consistent generative adversarial networks for pan sharpening","volume":"60","author":"Zhou","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"4","key":"10.1016\/j.neunet.2023.10.058_b49","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1109\/MGRS.2020.3046356","article-title":"Deep learning meets SAR: Concepts, models, pitfalls, and perspectives","volume":"9","author":"Zhu","year":"2021","journal-title":"IEEE Geoscience and Remote Sensing Magazine"},{"issue":"4","key":"10.1016\/j.neunet.2023.10.058_b50","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geoscience and Remote Sensing Magazine"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608023006238?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608023006238?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:56:20Z","timestamp":1760237780000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608023006238"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":50,"alternative-id":["S0893608023006238"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2023.10.058","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2024,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A benchmarking protocol for SAR colorization: From regression to deep learning approaches","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2023.10.058","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}]}}