{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:20:56Z","timestamp":1761582056480,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T00:00:00Z","timestamp":1612224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972060","U1713213","62027827"],"award-info":[{"award-number":["61972060","U1713213","62027827"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFE0110800"],"award-info":[{"award-number":["2019YFE0110800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["cstc2020jcyj-zdxmX0025","cstc2019cxcyljrc-td0270"],"award-info":[{"award-number":["cstc2020jcyj-zdxmX0025","cstc2019cxcyljrc-td0270"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chongqing Graduate Student Research and Innovation Project","award":["CYS20255"],"award-info":[{"award-number":["CYS20255"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the rapid development of deep neural networks in the field of remote sensing image fusion, the pan-sharpening method based on convolutional neural networks has achieved remarkable effects. However, because remote sensing images contain complex features, existing methods cannot fully extract spatial features while maintaining spectral quality, resulting in insufficient reconstruction capabilities. To produce high-quality pan-sharpened images, a multiscale perception dense coding convolutional neural network (MDECNN) is proposed. The network is based on dual-stream input, designing multiscale blocks to separately extract the rich spatial information contained in panchromatic (PAN) images, designing feature enhancement blocks and dense coding structures to fully learn the feature mapping relationship, and proposing comprehensive loss constraint expectations. Spectral mapping is used to maintain spectral quality and obtain high-quality fused images. Experiments on different satellite datasets show that this method is superior to the existing methods in both subjective and objective evaluations.<\/jats:p>","DOI":"10.3390\/rs13030535","type":"journal-article","created":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T13:01:12Z","timestamp":1612270872000},"page":"535","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["MDECNN: A Multiscale Perception Dense Encoding Convolutional Neural Network for Multispectral Pan-Sharpening"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9033-8245","authenticated-orcid":false,"given":"Weisheng","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Xuesong","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Meilin","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/S1566-2535(01)00036-7","article-title":"A new look at IHS-like image fusion methods","volume":"2","author":"Tu","year":"2001","journal-title":"Inf. Fusion"},{"key":"ref_2","first-page":"339","article-title":"Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis. Photogramm. Eng","volume":"55","author":"Kwarteng","year":"1989","journal-title":"Remote Sens."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TGRS.2010.2051674","article-title":"A new adaptive component-substitution-based satellite image fusion by using partial replacement","volume":"49","author":"Choi","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1109\/TGRS.2016.2614367","article-title":"Context-Adaptive Pansharpening Based on Image Segmentation","volume":"55","author":"Restaino","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","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_7","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_8","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_9","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_10","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/LGRS.2007.909934","article-title":"Indusion: Fusion of Multispectral and Panchromatic Images Using the Induction Scaling Technique","volume":"5","author":"Khan","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.inffus.2006.02.001","article-title":"Remote sensing image fusion using the curvelet transform","volume":"8","author":"Nencini","year":"2007","journal-title":"Inf. Fusion"},{"key":"ref_12","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 Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Burger, H.C., Schuler, C.J., and Harmeling, S. (2012, January 16\u201321). Image denoising: Can plain neural networks compete with BM3D?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6247952"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5116","DOI":"10.1109\/TGRS.2011.2158607","article-title":"An image fusion approach based on Markov random fields","volume":"49","author":"Xu","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","first-page":"43","article-title":"A variational model for P+ XS image fusion","volume":"69","author":"Ballester","year":"2006","journal-title":"Int. J. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1109\/LGRS.2019.2926681","article-title":"Model-Based Reduced-Rank Pansharpening","volume":"17","author":"Palsson","year":"2019","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1109\/TSMCB.2012.2198810","article-title":"Adjustable model-based fusion method for multispectral and panchromatic images","volume":"42","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1109\/TGRS.2010.2067219","article-title":"A new pan-sharpening method using a compressed sensing technique","volume":"49","author":"Li","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2194","DOI":"10.1109\/TGRS.2015.2497309","article-title":"A compressed-sensing-based pan-sharpening method for spectral distortion reduction","volume":"54","author":"Ghahremani","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4779","DOI":"10.1109\/TGRS.2012.2230332","article-title":"Remote sensing image fusion via sparse representations over learned dictionaries","volume":"51","author":"Li","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.dsp.2018.04.002","article-title":"Multimodal image fusion using sparse representation classification in tetrolet domain","volume":"79","author":"Shahdoosti","year":"2018","journal-title":"Digit. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2014, January 6\u201312). Learning a deep convolutional network for image super-resolution. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_23","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_24","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_25","doi-asserted-by":"crossref","unstructured":"Yang, J., Fu, X., Hu, Y., Huang, Y., Ding, X., and Paisley, J. (2017, January 22\u201329). PanNet: A deep network architecture for pan-sharpening. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.193"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1109\/LGRS.2010.2046715","article-title":"An adaptive IHS pan-sharpening method","volume":"7","author":"Rahmani","year":"2010","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shi, Y., Wanyu, Z., and Wei, L. (2019, January 11\u201313). Pansharpening of Multispectral Images based on Cycle-spinning Quincunx Lifting Transform. Proceedings of the IEEE International Conference on Signal, Information and Data Processing, Chongqing, China.","DOI":"10.1109\/ICSIDP47821.2019.9172997"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1016\/j.camwa.2005.08.009","article-title":"The loss of orthogonality in the Gram-Schmidt orthogonalization process","volume":"50","author":"Giraud","year":"2005","journal-title":"Comput. Math. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.apgeog.2012.10.008","article-title":"Evaluation of pansharpening algorithms in support of earth observation based rapid-mapping workflows","volume":"37","author":"Witharana","year":"2013","journal-title":"Appl. Geogr."},{"key":"ref_30","first-page":"1","article-title":"Deep Multiscale Detail Networks for Multiband Spectral Image Sharpening","volume":"99","author":"Fu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11220-016-0135-6","article-title":"Remote sensing image fusion with convolutional neural network","volume":"17","author":"Zhong","year":"2016","journal-title":"Sens. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1109\/JSTARS.2018.2794888","article-title":"A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening","volume":"11","author":"Yuan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kim, J., and Lee, J.K. (2016, January 27\u201330). Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ledig, C. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27\u201330). Deeply-recursive convolutional network for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.181"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., and Liu, X. (2017, January 21\u201326). Image super-resolution via deep recursive residual network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.298"},{"key":"ref_38","unstructured":"Dong, C., Loy, C., and Tang, X. Accelerating the super-resolution convolutional neural network. Proceedings of the European Conference on Computer Vision."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1109\/JSTARS.2013.2283236","article-title":"Two-step sparse coding for the pan-sharpening of remote sensing images","volume":"7","author":"Jiang","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1109\/LGRS.2014.2376034","article-title":"A new pan-sharpening method with deep neural networks","volume":"12","author":"Huang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rao, Y., He, L., and Zhu, J. (2017, January 19\u201321). A residual convolutional neural network for pan-shaprening. Proceedings of the International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958807"},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Azarang, A., and Ghassemian, H. (2017, January 19\u201320). A new pansharpening method using multi resolution analysis framework and deep neural networks. Proceedings of the 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), Shahrekord, Iran.","DOI":"10.1109\/PRIA.2017.7983017"},{"key":"ref_44","unstructured":"Vitale, S. (August, January 28). A CNN-based Pansharpening Method with Perceptual Loss. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan."},{"key":"ref_45","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_46","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_47","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., and Funkhouser, T. (2017, January 21\u201326). Dilated residual networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.75"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3752","DOI":"10.1109\/TIP.2019.2902115","article-title":"DECODE: Deep confidence network for robust image classification","volume":"28","author":"Ding","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Liu, X., Wang, Y., and Liu, Q. (2018, January 7\u201310). PSGAN: A generative adversarial network for remote sensing image pan-sharpening. Proceedings of the IEEE International Conference on Image Processing, Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451049"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/535\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:19:07Z","timestamp":1760159947000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/535"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,2]]},"references-count":51,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13030535"],"URL":"https:\/\/doi.org\/10.3390\/rs13030535","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,2,2]]}}}