{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:19:06Z","timestamp":1753600746623,"version":"3.37.3"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"17-18","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61601266"],"award-info":[{"award-number":["61601266"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s10489-024-05580-1","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T09:01:50Z","timestamp":1718787710000},"page":"8041-8058","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dual-branch and triple-attention network for pan-sharpening"],"prefix":"10.1007","volume":"54","author":[{"given":"Wenhao","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3368-6013","authenticated-orcid":false,"given":"Mingliang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdellah","family":"Chehri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzhe","family":"Zhai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qilei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gwanggil","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"issue":"2","key":"5580_CR1","doi-asserted-by":"crossref","first-page":"193","DOI":"10.14358\/PERS.74.2.193","volume":"74","author":"L Alparone","year":"2008","unstructured":"Alparone L, Aiazzi B, Baronti S, Garzelli A, Nencini F, Selva M (2008) Multispectral and panchromatic data fusion assessment without reference. Photogramm Eng Remote Sensing 74(2):193\u2013200","journal-title":"Photogramm Eng Remote Sensing"},{"issue":"10","key":"5580_CR2","doi-asserted-by":"publisher","first-page":"3012","DOI":"10.1109\/TGRS.2007.904923","volume":"45","author":"L Alparone","year":"2007","unstructured":"Alparone L, Wald L, Chanussot J, Thomas C, Gamba P, Bruce LM (2007) Comparison of pansharpening algorithms: Outcome of the 2006 grs-s data-fusion contest. IEEE Trans Geosci Remote Sens 45(10):3012\u20133021. https:\/\/doi.org\/10.1109\/TGRS.2007.904923","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5580_CR3","doi-asserted-by":"crossref","first-page":"110968","DOI":"10.1016\/j.rse.2018.11.011","volume":"238","author":"EL Bullock","year":"2020","unstructured":"Bullock EL, Woodcock CE, Olofsson P (2020) Monitoring tropical forest degradation using spectral unmixing and landsat time series analysis. Remote Sens Environ 238:110968","journal-title":"Remote Sens Environ"},{"key":"5580_CR4","doi-asserted-by":"crossref","unstructured":"Cao X, Chen Y, Cao W (2022) Proximal pannet: A model-based deep network for pansharpening. In: Proceedings of the AAAI conference on artificial intelligence vol\u00a036, pp 176\u2013184","DOI":"10.1609\/aaai.v36i1.19892"},{"key":"5580_CR5","doi-asserted-by":"crossref","first-page":"105919","DOI":"10.1016\/j.asoc.2019.105919","volume":"86","author":"Y Chen","year":"2020","unstructured":"Chen Y, Peng G, Zhu Z, Li S (2020) A novel deep learning method based on attention mechanism for bearing remaining useful life prediction. Appl Soft Comput 86:105919","journal-title":"Appl Soft Comput"},{"key":"5580_CR6","unstructured":"Choromanski KM, Likhosherstov V, Dohan D, Song X, Gane A, Sarlos T, Hawkins P, Davis JQ, Mohiuddin A, Kaiser L, Belanger DB, Colwell LJ, Weller A (2021) Rethinking attention with performers. In: International conference on learning representations"},{"key":"5580_CR7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3163887","volume":"60","author":"M Ciotola","year":"2022","unstructured":"Ciotola M, Vitale S, Mazza A, Poggi G, Scarpa G (2022) Pansharpening by convolutional neural networks in the full resolution framework. IEEE Trans Geosci Remote Sens 60:1\u201317","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"8","key":"5580_CR8","doi-asserted-by":"crossref","first-page":"6995","DOI":"10.1109\/TGRS.2020.3031366","volume":"59","author":"LJ Deng","year":"2020","unstructured":"Deng LJ, Vivone G, Jin C, Chanussot J (2020) Detail injection-based deep convolutional neural networks for pansharpening. IEEE Trans Geosci Remote Sens 59(8):6995\u20137010","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"3","key":"5580_CR9","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1109\/MGRS.2022.3187652","volume":"10","author":"LJ Deng","year":"2022","unstructured":"Deng LJ, Vivone G, Paoletti ME, Scarpa G, He J, Zhang Y, Chanussot J, Plaza A (2022) Machine learning in pansharpening: A benchmark, from shallow to deep networks. IEEE Geosci Remote Sens Mag 10(3):279\u2013315","journal-title":"IEEE Geosci Remote Sens Mag"},{"issue":"2","key":"5580_CR10","first-page":"295","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong C, Loy CC, He K, Tang X (2015) Image super-resolution using deep convolutional networks. IEEE Geosci Remote Sens Mag 38(2):295\u2013307","journal-title":"IEEE Geosci Remote Sens Mag"},{"key":"5580_CR11","doi-asserted-by":"crossref","unstructured":"Fu X, Lin Z, Huang Y, Ding X (2019) A variational pan-sharpening with local gradient constraints. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10265\u201310274","DOI":"10.1109\/CVPR.2019.01051"},{"key":"5580_CR12","doi-asserted-by":"crossref","unstructured":"Gao H, Li S, Li J, Dian R (2023) Multispectral image pan-sharpening guided by component substitution model. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2023.3309863"},{"key":"5580_CR13","doi-asserted-by":"crossref","unstructured":"Gillespie AR, Kahle AB, Walker RE (1987) Color enhancement of highly correlated images. ii. channel ratio and \u201cchromaticity\u201d transformation techniques. Remote Sens Environ 22(3):343\u2013365","DOI":"10.1016\/0034-4257(87)90088-5"},{"key":"5580_CR14","first-page":"1","volume":"60","author":"M Gong","year":"2022","unstructured":"Gong M, Ma J, Xu H, Tian X, Zhang XP (2022) D2tnet: A convlstm network with dual-direction transfer for pan-sharpening. IEEE Trans Geosci Remote Sens 60:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"5580_CR15","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/TGRS.2019.2930246","volume":"58","author":"Y Gong","year":"2019","unstructured":"Gong Y, Xiao Z, Tan X, Sui H, Xu C, Duan H, Li D (2019) Context-aware convolutional neural network for object detection in vhr remote sensing imagery. IEEE Trans Geosci Remote Sens 58(1):34\u201344","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"3","key":"5580_CR16","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","volume":"8","author":"MH Guo","year":"2022","unstructured":"Guo MH, Xu TX, Liu JJ, Liu ZN, Jiang PT, Mu TJ, Zhang SH, Martin RR, Cheng MM, Hu SM (2022) Attention mechanisms in computer vision: A survey. Comput Vis media 8(3):331\u2013368","journal-title":"Comput Vis media"},{"issue":"2","key":"5580_CR17","doi-asserted-by":"crossref","first-page":"867","DOI":"10.46488\/NEPT.2022.v21i02.050","volume":"21","author":"F Hashim","year":"2022","unstructured":"Hashim F, Dibs H, Jaber HS (2022) Adopting gram-schmidt and brovey methods for estimating land use and land cover using remote sensing and satellite images. Nat Environ Pollut Technol 21(2):867\u2013881","journal-title":"Nat Environ Pollut Technol"},{"issue":"4","key":"5580_CR18","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1109\/JSTARS.2019.2898574","volume":"12","author":"L He","year":"2019","unstructured":"He L, Rao Y, Li J, Chanussot J, Plaza A, Zhu J, Li B (2019) Pansharpening via detail injection based convolutional neural networks. IEEE J Sel Top Appl Earth Obs Remote Sens 12(4):1188\u20131204. https:\/\/doi.org\/10.1109\/JSTARS.2019.2898574","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"issue":"5","key":"5580_CR19","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1109\/LGRS.2014.2376034","volume":"12","author":"W Huang","year":"2015","unstructured":"Huang W, Xiao L, Wei Z, Liu H, Tang S (2015) A new pan-sharpening method with deep neural networks. IEEE Geosci Remote Sens Lett 12(5):1037\u20131041","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"5580_CR20","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.isprsjprs.2020.11.001","volume":"171","author":"FD Javan","year":"2021","unstructured":"Javan FD, Samadzadegan F, Mehravar S, Toosi A, Khatami R, Stein A (2021) A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery. ISPRS J Photogramm Remote Sens 171:101\u2013117","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"5580_CR21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.3022438","volume":"70","author":"L Jian","year":"2020","unstructured":"Jian L, Yang X, Liu Z, Jeon G, Gao M, Chisholm D (2020) Sedrfuse: A symmetric encoder-decoder with residual block network for infrared and visible image fusion. IEEE Trans Instrum Meas 70:1\u201315","journal-title":"IEEE Trans Instrum Meas"},{"key":"5580_CR22","unstructured":"Jianwen H, Zeping W, Pei H (2023) A review of pansharpening methods based on deep learning. Remote Sensing for Natural Resources 35(1)"},{"key":"5580_CR23","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.inffus.2021.09.002","volume":"78","author":"C Jin","year":"2022","unstructured":"Jin C, Deng LJ, Huang TZ, Vivone G (2022) Laplacian pyramid networks: A new approach for multispectral pansharpening. Inf Fusion 78:158\u2013170","journal-title":"Inf Fusion"},{"key":"5580_CR24","doi-asserted-by":"crossref","unstructured":"Jin ZR, Zhang TJ, Jiang TX, Vivone G, Deng LJ (2022) Lagconv: Local-context adaptive convolution kernels with global harmonic bias for pansharpening. Proceedings of the AAAI conference on artificial intelligence vol\u00a036, pp 1113\u20131121","DOI":"10.1609\/aaai.v36i1.19996"},{"key":"5580_CR25","doi-asserted-by":"crossref","unstructured":"Lee J, Seo S, Kim M (2021) Sipsa-net: Shift-invariant pan sharpening with moving object alignment for satellite imagery. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10166\u201310174","DOI":"10.1109\/CVPR46437.2021.01003"},{"issue":"5","key":"5580_CR26","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1109\/LGRS.2013.2284282","volume":"11","author":"Y Leung","year":"2013","unstructured":"Leung Y, Liu J, Zhang J (2013) An improved adaptive intensity-hue-saturation method for the fusion of remote sensing images. IEEE Geosci Remote Sens Lett 11(5):985\u2013989","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"5580_CR27","doi-asserted-by":"crossref","first-page":"27163","DOI":"10.1109\/ACCESS.2020.2971502","volume":"8","author":"X Li","year":"2020","unstructured":"Li X, Xu F, Lyu X, Tong Y, Chen Z, Li S, Liu D (2020) A remote-sensing image pan-sharpening method based on multi-scale channel attention residual network. IEEE Access 8:27163\u201327177","journal-title":"IEEE Access"},{"key":"5580_CR28","first-page":"1","volume":"19","author":"Y Liang","year":"2022","unstructured":"Liang Y, Zhang P, Mei Y, Wang T (2022) Pmacnet: Parallel multiscale attention constraint network for pan-sharpening. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"12","key":"5580_CR29","doi-asserted-by":"crossref","first-page":"10227","DOI":"10.1109\/TGRS.2020.3042974","volume":"59","author":"Q Liu","year":"2020","unstructured":"Liu Q, Zhou H, Xu Q, Liu X, Wang Y (2020) Psgan: A generative adversarial network for remote sensing image pan-sharpening. IEEE Trans Geosci Remote Sens 59(12):10227\u201310242","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5580_CR30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2019.07.010","volume":"55","author":"X Liu","year":"2020","unstructured":"Liu X, Liu Q, Wang Y (2020) Remote sensing image fusion based on two-stream fusion network. Inf Fusion 55:1\u201315","journal-title":"Inf Fusion"},{"key":"5580_CR31","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.inffus.2020.04.006","volume":"62","author":"J Ma","year":"2020","unstructured":"Ma J, Yu W, Chen C, Liang P, Guo X, Jiang J (2020) Pan-gan: An unsupervised pan-sharpening method for remote sensing image fusion. Inf Fusion 62:110\u2013120","journal-title":"Inf Fusion"},{"issue":"7","key":"5580_CR32","doi-asserted-by":"crossref","first-page":"594","DOI":"10.3390\/rs8070594","volume":"8","author":"G Masi","year":"2016","unstructured":"Masi G, Cozzolino D, Verdoliva L, Scarpa G (2016) Pansharpening by convolutional neural networks. Remote Sensing 8(7):594","journal-title":"Remote Sensing"},{"key":"5580_CR33","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.inffus.2018.05.006","volume":"46","author":"X Meng","year":"2019","unstructured":"Meng X, Shen H, Li H, Zhang L, Fu R (2019) Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: Practical discussion and challenges. Inf Fusion 46:102\u2013113","journal-title":"Inf Fusion"},{"key":"5580_CR34","doi-asserted-by":"crossref","unstructured":"Menon AS, Aravinth J, Veni S (2023) Pan-sharpening of multi-spectral remote sensing data using multi-resolution analysis. In: Machine intelligence techniques for data analysis and signal processing: proceedings of the 4th international conference MISP 2022, vol 1, pp 697\u2013705. Springer","DOI":"10.1007\/978-981-99-0085-5_56"},{"key":"5580_CR35","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 807\u2013814"},{"issue":"2","key":"5580_CR36","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.inffus.2006.02.001","volume":"8","author":"F Nencini","year":"2007","unstructured":"Nencini F, Garzelli A, Baronti S, Alparone L (2007) Remote sensing image fusion using the curvelet transform. Inf Fusion 8(2):143\u2013156","journal-title":"Inf Fusion"},{"key":"5580_CR37","doi-asserted-by":"crossref","first-page":"36322","DOI":"10.1109\/ACCESS.2019.2905015","volume":"7","author":"Z Pan","year":"2019","unstructured":"Pan Z, Yu W, Yi X, Khan A, Yuan F, Zheng Y (2019) Recent progress on generative adversarial networks (gans): A survey. IEEE access 7:36322\u201336333","journal-title":"IEEE access"},{"issue":"4","key":"5580_CR38","doi-asserted-by":"crossref","first-page":"3192","DOI":"10.1109\/TGRS.2020.3009207","volume":"59","author":"Y Qu","year":"2020","unstructured":"Qu Y, Baghbaderani RK, Qi H, Kwan C (2020) Unsupervised pansharpening based on self-attention mechanism. IEEE Trans Geosci Remote Sens 59(4):3192\u20133208","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"5","key":"5580_CR39","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1109\/JSTARS.2018.2805923","volume":"11","author":"Z Shao","year":"2018","unstructured":"Shao Z, Cai J (2018) Remote sensing image fusion with deep convolutional neural network. IEEE J Sel Top Appl Earth Obs Remote Sens 11(5):1656\u20131669","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"5580_CR40","first-page":"100963","volume":"30","author":"KV Sharma","year":"2023","unstructured":"Sharma KV, Kumar V, Singh K, Mehta DJ (2023) Landsat 8 lst pan sharpening using novel principal component based downscaling model. Remote Sens Appl: Soc Environ 30:100963","journal-title":"Remote Sens Appl: Soc Environ"},{"key":"5580_CR41","first-page":"1","volume":"60","author":"X Su","year":"2022","unstructured":"Su X, Li J, Hua Z (2022) Transformer-based regression network for pansharpening remote sensing images. IEEE Trans Geosci Remote Sens 60:1\u201323","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"5","key":"5580_CR42","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.1109\/TCYB.2022.3208095","volume":"53","author":"Y Su","year":"2022","unstructured":"Su Y, Zhu H, Wong KC, Chang Y, Li X (2022) Hyperspectral image denoising via weighted multidirectional low-rank tensor recovery. IEEE Trans Cybern 53(5):2753\u20132766","journal-title":"IEEE Trans Cybern"},{"key":"5580_CR43","doi-asserted-by":"crossref","unstructured":"Suryanarayana G, Saidulu B, Priya MRH, Likhitha K, Pragathi K, Srikanth K (2022) Fusion of hyperspectral and multispectral images based on principal component analysis and guided bilateral filtering. International Journal of System Assurance Engineering and Management, pp 1\u201310","DOI":"10.1007\/s13198-022-01767-2"},{"issue":"4","key":"5580_CR44","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/s40745-022-00389-6","volume":"9","author":"A Tang","year":"2022","unstructured":"Tang A, Quan P, Niu L, Shi Y (2022) A survey for sparse regularization based compression methods. Ann Data Sci 9(4):695\u2013722","journal-title":"Ann Data Sci"},{"key":"5580_CR45","doi-asserted-by":"crossref","first-page":"2030","DOI":"10.1109\/JSTARS.2021.3051569","volume":"14","author":"X Tang","year":"2021","unstructured":"Tang X, Ma Q, Zhang X, Liu F, Ma J, Jiao L (2021) Attention consistent network for remote sensing scene classification. IEEE J Sel Top Appl Earth Obs Remote Sens 14:2030\u20132045","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"issue":"4","key":"5580_CR46","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/LGRS.2004.834804","volume":"1","author":"TM Tu","year":"2004","unstructured":"Tu TM, Huang PS, Hung CL, Chang CP (2004) A fast intensity-hue-saturation fusion technique with spectral adjustment for ikonos imagery. IEEE Geosci Remote Sens Lett 1(4):309\u2013312","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"5580_CR47","first-page":"691","volume":"63","author":"L Wald","year":"1997","unstructured":"Wald L, Ranchin T, Mangolini M (1997) Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images. Photogramm Eng Remote Sensing 63:691\u2013699","journal-title":"Photogramm Eng Remote Sensing"},{"issue":"3","key":"5580_CR48","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/97.995823","volume":"9","author":"Z Wang","year":"2002","unstructured":"Wang Z, Bovik AC (2002) A universal image quality index. IEEE Signal Process Lett 9(3):81\u201384","journal-title":"IEEE Signal Process Lett"},{"key":"5580_CR49","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.inffus.2022.09.008","volume":"90","author":"Z Wang","year":"2023","unstructured":"Wang Z, Ma Y, Zhang Y (2023) Review of pixel-level remote sensing image fusion based on deep learning. Inf Fusion 90:36\u201358","journal-title":"Inf Fusion"},{"issue":"5","key":"5580_CR50","doi-asserted-by":"crossref","first-page":"2503","DOI":"10.1109\/TGRS.2017.2742002","volume":"56","author":"Y Xing","year":"2018","unstructured":"Xing Y, Wang M, Yang S, Zhang K (2018) Pansharpening with multiscale geometric support tensor machine. IEEE Trans Geosci Remote Sens 56(5):2503\u20132517","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5580_CR51","doi-asserted-by":"crossref","unstructured":"Xing Y, Zhang Y, Zhang Y (2022) Wavefusion: Wavelet assistant fusion model for pan-sharpening. In: IGARSS 2022-2022 IEEE international geoscience and remote sensing symposium, pp 1083\u20131086. IEEE","DOI":"10.1109\/IGARSS46834.2022.9884867"},{"key":"5580_CR52","doi-asserted-by":"crossref","unstructured":"Xiong Z, Liu N, Wang N, Sun Z, Li W (2023) Unsupervised pansharpening method using residual network with spatial texture attention. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2023.3267056"},{"key":"5580_CR53","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.inffus.2022.10.001","volume":"91","author":"Q Xu","year":"2023","unstructured":"Xu Q, Li Y, Nie J, Liu Q, Guo M (2023) Upangan: Unsupervised pansharpening based on the spectral and spatial loss constrained generative adversarial network. Inf Fusion 91:31\u201346","journal-title":"Inf Fusion"},{"key":"5580_CR54","first-page":"1","volume":"60","author":"K Yan","year":"2022","unstructured":"Yan K, Zhou M, Liu L, Xie C, Hong D (2022) When pansharpening meets graph convolution network and knowledge distillation. IEEE Trans Geosci Remote Sens 60:1\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5580_CR55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2021.10.001","volume":"79","author":"CS Yilmaz","year":"2022","unstructured":"Yilmaz CS, Yilmaz V, Gungor O (2022) A theoretical and practical survey of image fusion methods for multispectral pansharpening. Inf Fusion 79:1\u201343","journal-title":"Inf Fusion"},{"issue":"3","key":"5580_CR56","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1109\/JSTARS.2018.2794888","volume":"11","author":"Q Yuan","year":"2018","unstructured":"Yuan Q, Wei Y, Meng X, Shen H, Zhang L (2018) A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening. IEEE J Sel Top Appl Earth Obs Remote Sens 11(3):978\u2013989. https:\/\/doi.org\/10.1109\/JSTARS.2018.2794888","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"5580_CR57","unstructured":"Yuhas RH, Goetz AFH, Boardman JW (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. vol 1: AVIRIS Workshop"},{"issue":"2","key":"5580_CR58","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1007\/s10586-022-03749-2","volume":"26","author":"W Zhai","year":"2023","unstructured":"Zhai W, Gao M, Souri A, Li Q, Guo X, Shang J, Zou G (2023) An attentive hierarchy convnet for crowd counting in smart city. Cluster Comput 26(2):1099\u20131111","journal-title":"Cluster Comput"},{"issue":"8","key":"5580_CR59","doi-asserted-by":"crossref","first-page":"5813","DOI":"10.1109\/TGRS.2019.2902568","volume":"57","author":"H Zhang","year":"2019","unstructured":"Zhang H, Li Y, Jiang Y, Wang P, Shen Q, Shen C (2019) Hyperspectral classification based on lightweight 3-d-cnn with transfer learning. IEEE Trans Geosci Remote Sens 57(8):5813\u20135828","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5580_CR60","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.isprsjprs.2020.12.014","volume":"172","author":"H Zhang","year":"2021","unstructured":"Zhang H, Ma J (2021) Gtp-pnet: A residual learning network based on gradient transformation prior for pansharpening. ISPRS J Photogramm Remote Sens 172:223\u2013239","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"5580_CR61","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.inffus.2021.06.008","volume":"76","author":"H Zhang","year":"2021","unstructured":"Zhang H, Xu H, Tian X, Jiang J, Ma J (2021) Image fusion meets deep learning: A survey and perspective. Inf Fusion 76:323\u2013336","journal-title":"Inf Fusion"},{"key":"5580_CR62","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. In: Proceedings of the european conference on computer vision (ECCV), pp 286\u2013301","DOI":"10.1007\/978-3-030-01234-2_18"},{"issue":"8","key":"5580_CR63","doi-asserted-by":"publisher","first-page":"5549","DOI":"10.1109\/TGRS.2019.2900419","volume":"57","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Liu C, Sun M, Ou Y (2019) Pan-sharpening using an efficient bidirectional pyramid network. IEEE Trans Geosci Remote Sens 57(8):5549\u20135563. https:\/\/doi.org\/10.1109\/TGRS.2019.2900419","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"8","key":"5580_CR64","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.1109\/LGRS.2019.2945424","volume":"17","author":"Y Zheng","year":"2019","unstructured":"Zheng Y, Li J, Li Y, Cao K, Wang K (2019) Deep residual learning for boosting the accuracy of hyperspectral pansharpening. IEEE Geosci Remote Sens Lett 17(8):1435\u20131439","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"6","key":"5580_CR65","doi-asserted-by":"crossref","first-page":"2867","DOI":"10.1109\/JSTARS.2017.2697445","volume":"10","author":"S Zhong","year":"2017","unstructured":"Zhong S, Zhang Y, Chen Y, Wu D (2017) Combining component substitution and multiresolution analysis: A novel generalized bdsd pansharpening algorithm. IEEE J Sel Top Appl Earth Obs Remote Sens 10(6):2867\u20132875","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"5580_CR66","first-page":"1","volume":"60","author":"M Zhou","year":"2022","unstructured":"Zhou M, Huang J, Fu X, Zhao F, Hong D (2022) Effective pan-sharpening by multiscale invertible neural network and heterogeneous task distilling. IEEE Trans Geosci Remote Sens 60:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5580_CR67","doi-asserted-by":"crossref","unstructured":"Zhou M, Huang J, Yan K, Yu H, Fu X, Liu A, Wei X, Zhao F (2022) Spatial-frequency domain information integration for pan-sharpening. In: European conference on computer vision, pp 274\u2013291. Springer (2022)","DOI":"10.1007\/978-3-031-19797-0_16"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05580-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05580-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05580-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T12:25:12Z","timestamp":1723033512000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05580-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,19]]},"references-count":67,"journal-issue":{"issue":"17-18","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["5580"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05580-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2024,6,19]]},"assertion":[{"value":"31 May 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing of interest"}}]}}