{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:21:37Z","timestamp":1761582097544,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,6,5]],"date-time":"2021-06-05T00:00:00Z","timestamp":1622851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In most practical applications of remote sensing images, high-resolution multispectral images are needed. Pansharpening aims to generate high-resolution multispectral (MS) images from the input of high spatial resolution single-band panchromatic (PAN) images and low spatial resolution multispectral images. Inspired by the remarkable results of other researchers in pansharpening based on deep learning, we propose a multilevel dense connection network with a feedback connection. Our network consists of four parts. The first part consists of two identical subnetworks to extract features from PAN and MS images. The second part is a multilevel feature fusion and recovery network, which is used to fuse images in the feature domain and to encode and decode features at different levels so that the network can fully capture different levels of information. The third part is a continuous feedback operation, which refines shallow features by feedback. The fourth part is an image reconstruction network. High-quality images are recovered by making full use of multistage decoding features through dense connections. Experiments on different satellite datasets show that our proposed method is superior to existing methods, through subjective visual evaluation and objective evaluation indicators. Compared with the results of other models, our results achieve significant gains on the multiple objective index values used to measure the spectral quality and spatial details of the generated image, namely spectral angle mapper (SAM), relative global dimensional synthesis error (ERGAS), and structural similarity (SSIM).<\/jats:p>","DOI":"10.3390\/rs13112218","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T01:56:40Z","timestamp":1623031000000},"page":"2218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MDCwFB: A Multilevel Dense Connection Network with Feedback Connections for Pansharpening"],"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":"Minghao","family":"Xiang","sequence":"additional","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"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,5]]},"reference":[{"key":"ref_1","first-page":"1699","article-title":"Remote sensing geological survey technology and application research","volume":"85","author":"Wang","year":"2011","journal-title":"Acta Geol. Sin."},{"key":"ref_2","first-page":"45","article-title":"Remote Sensing Research on Characteristics of Mine Geological Hazards","volume":"1","author":"Li","year":"2005","journal-title":"Adv. Earth Sci."},{"key":"ref_3","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_4","first-page":"339","article-title":"Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis","volume":"55","author":"Kwarteng","year":"1989","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_5","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_6","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_7","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_8","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_9","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TCOM.1983.1095851","article-title":"The Laplacian pyramid as a compact image code","volume":"3","author":"Burt","year":"1983","journal-title":"IEEE Trans. Commun."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"4131","DOI":"10.1080\/01431161.2015.1071897","article-title":"Remote-sensing image fusion based on Curvelets and ICA","volume":"36","author":"Ghahremani","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"2006","DOI":"10.1016\/j.patrec.2005.02.010","article-title":"Color transfer based remote sensing image fusion using nonseparable wavelet frame transform","volume":"26","author":"Li","year":"2005","journal-title":"Pattern Recognit. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1109\/JSTSP.2015.2407855","article-title":"Bayesian fusion of multi-band images","volume":"9","author":"Wei","year":"2015","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.1109\/JSTARS.2014.2310781","article-title":"An Online Coupled Dictionary Learning Approach for Remote Sensing Image Fusion","volume":"7","author":"Guo","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_16","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_17","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_18","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_19","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_20","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_21","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1109\/JSTARS.2019.2898574","article-title":"Pansharpening via Detail Injection Based Convolutional Neural Networks","volume":"12","author":"He","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_24","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_26","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_27","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_28","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van, 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_29","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., and Huszar, F. (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_31","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., and Wu, S. (2018, January 8\u201314). ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, Z., Yang, J., Liu, Z., Yang, X., Jeon, G., and Wu, W. (2019, January 15\u201320). Feedback Network for Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00399"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, Q., and Wang, Y. (2018, January 5\u20137). Remote sensing image fusion based on two-stream fusion network. Proceedings of the 24th International Conference on Multimedia Modeling, Bangkok, Thailand.","DOI":"10.1007\/978-3-319-73603-7_35"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2019.07.010","article-title":"Remote sensing image fusion based on two-stream fusion network","volume":"55","author":"Liu","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_36","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"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1109\/LGRS.2019.2949745","article-title":"Residual encoder-decoder conditional generative adversarial network for pansharpening","volume":"17","author":"Shao","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Roy, A.G., Navab, N., and Wachinger, C. (2018). Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, Springer.","DOI":"10.1007\/978-3-030-00928-1_48"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M., and Tajbakhsh, N. (2018). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fu, S., Meng, W., and Jeon, G. (2020). Two-Path Network with Feedback Connections for Pan-Sharpening in Remote Sensing. Remote Sens., 12.","DOI":"10.3390\/rs12101674"},{"key":"ref_41","unstructured":"Yuhas, R.H., Goetz, A.F., and Boardman, J.W. (1992, January 1\u20135). Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm. Proceedings of the Summaries 3rd Annual JPL Airborne Geoscience Workshop, Pasadena, CA, USA."},{"key":"ref_42","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_43","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 Trans. Image Process."},{"key":"ref_44","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_45","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2218\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:11:17Z","timestamp":1760163077000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2218"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,5]]},"references-count":45,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13112218"],"URL":"https:\/\/doi.org\/10.3390\/rs13112218","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,6,5]]}}}