{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T10:11:58Z","timestamp":1768817518584,"version":"3.49.0"},"reference-count":65,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hainan Provincial Natural Science Foundation of China","award":["2019CXTD400"],"award-info":[{"award-number":["2019CXTD400"]}]},{"name":"National Key Research and Development Program of China","award":["2018YFB1404400"],"award-info":[{"award-number":["2018YFB1404400"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing images have been widely applied in various industries; nevertheless, the resolution of such images is relatively low. Panchromatic sharpening (pan-sharpening) is a research focus in the image fusion domain of remote sensing. Pan-sharpening is used to generate high-resolution multispectral (HRMS) images making full use of low-resolution multispectral (LRMS) images and panchromatic (PAN) images. Traditional pan-sharpening has the problems of spectral distortion, ringing effect, and low resolution. The convolutional neural network (CNN) is gradually applied to pan-sharpening. Aiming at the aforementioned problems, we propose a distributed fusion framework based on residual CNN (RCNN), namely, RDFNet, which realizes the data fusion of three channels. It can make the most of the spectral information and spatial information of LRMS and PAN images. The proposed fusion network employs a distributed fusion architecture to make the best of the fusion outcome of the previous step in the fusion channel, so that the subsequent fusion acquires much more spectral and spatial information. Moreover, two feature extraction channels are used to extract the features of MS and PAN images respectively, using the residual module, and features of different scales are used for the fusion channel. In this way, spectral distortion and spatial information loss are reduced. Employing data from four different satellites to compare the proposed RDFNet, the results of the experiment show that the proposed RDFNet has superior performance in improving spatial resolution and preserving spectral information, and has good robustness and generalization in improving the fusion quality.<\/jats:p>","DOI":"10.3390\/rs13132556","type":"journal-article","created":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T02:44:39Z","timestamp":1625107479000},"page":"2556","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Distributed Fusion Framework of Multispectral and Panchromatic Images Based on Residual Network"],"prefix":"10.3390","volume":"13","author":[{"given":"Yuanyuan","family":"Wu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengxing","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchun","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siling","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.inffus.2016.03.003","article-title":"A review of remote sensing image fusion methods","volume":"32","author":"Ghassemian","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Qian, X., Lin, S., Cheng, G., Yao, X., Ren, H., and Wang, W. (2020). Object Detection in Remote Sensing Images Based on Improved Bounding Box Regression and Multi-Level Features Fusion. Remote Sens., 12.","DOI":"10.3390\/rs12010143"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.inffus.2016.05.004","article-title":"Pixel-level image fusion: A survey of the state of the art","volume":"33","author":"Li","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.inffus.2018.05.006","article-title":"Review of the Pansharpening Methods for Remote Sensing Images Based on the Idea of Meta-analysis: Practical Discussion and Challenges","volume":"46","author":"Meng","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/JSTARS.2020.3011992","article-title":"3D Channel and Spatial Attention Based Multi-Scale Spatial Spectral Residual Network for Hyperspectral Image Classification","volume":"13","author":"Lu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","first-page":"459","article-title":"The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data","volume":"56","author":"Carper","year":"1990","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/LGRS.2004.834804","article-title":"A Fast Intensity\u2013Hue\u2013Saturation Fusion Technique With Spectral Adjustment for IKONOS Imagery","volume":"1","author":"Tu","year":"2004","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1109\/TGRS.2004.825593","article-title":"Fusion of Multispectral and Panchromatic Images Using Improved IHS and PCA Mergers Based on Wavelet Decomposition","volume":"42","author":"Saleta","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.inffus.2015.06.006","article-title":"Combining the spectral PCA and spatial PCA fusion methods by an optimal filter","volume":"27","author":"Shahdoosti","year":"2016","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","first-page":"74","DOI":"10.1016\/j.inffus.2010.03.002","article-title":"Performance comparison of different multi-resolution transforms for image fusion","volume":"12","author":"Li","year":"2011","journal-title":"Inf. Fusion"},{"key":"ref_14","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_15","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_16","doi-asserted-by":"crossref","first-page":"3210","DOI":"10.1109\/TGRS.2014.2371812","article-title":"An Edge Preserving Multiresolution Fusion: Use of Contourlet Transform and MRF Prior","volume":"53","author":"Upla","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/LGRS.2010.2041323","article-title":"Multicontourlet-Based Adaptive Fusion of Infrared and Visible Remote Sensing Images","volume":"7","author":"Chang","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3089","DOI":"10.1109\/TIP.2006.877507","article-title":"The Nonsubsampled Contourlet Transform: Theory, Design, and Applications","volume":"15","author":"Cunha","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1080\/014311600750037499","article-title":"Smoothing Filter-based Intensity Modulation: A spectral preserve image fusion technique for improving spatial details","volume":"21","author":"Liu","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/LGRS.2013.2256875","article-title":"Sparse Representation Based Pansharpening Using Trained Dictionary","volume":"11","author":"Cheng","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1109\/LGRS.2014.2331291","article-title":"A Pansharpening Method Based on the Sparse Representation of Injected Details","volume":"12","author":"Vicinanza","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","first-page":"671","article-title":"The Laplacian Pyramid as a Compact Image Code","volume":"31","author":"Burt","year":"1987","journal-title":"Read Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6152","DOI":"10.1109\/TGRS.2020.2974806","article-title":"Pansharpening: Context-Based Generalized Laplacian Pyramids by Robust Regression","volume":"58","author":"Vivone","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"17633","DOI":"10.1007\/s11042-015-2879-8","article-title":"Image fusion method of SAR and infrared image based on Curvelet transform with adaptive weighting","volume":"76","author":"Ji","year":"2015","journal-title":"Multimed. Tools Appl."},{"key":"ref_28","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_29","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_30","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1109\/TGRS.2013.2253612","article-title":"Spatial and Spectral Image Fusion Using Sparse Matrix Factorization","volume":"52","author":"Huang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sun, L., He, C., Zheng, Y., and Tang, S. (2020). SLRL4D: Joint Restoration of Subspace Low-Rank Learning and Non-Local 4-D Transform Filtering for Hyperspectral Image. Remote Sens., 12.","DOI":"10.3390\/rs12182979"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1109\/TSMCB.2005.846647","article-title":"A learning-based method for image super-resolution from zoomed observations","volume":"35","author":"Joshi","year":"2005","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1109\/TIP.2016.2542360","article-title":"Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation","volume":"25","author":"Dong","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"594","DOI":"10.3390\/rs8070594","article-title":"Pansharpening by Convolutional Neural Networks","volume":"8","author":"Giuseppe","year":"2016","journal-title":"Remote Sens."},{"key":"ref_37","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 International Conference on Pattern Recognition and Image Analysis, Shahrekord, Iran.","DOI":"10.1109\/PRIA.2017.7983017"},{"key":"ref_38","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_39","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_40","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 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.193"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1109\/JSTARS.2018.2794888","article-title":"A Multi-Scale and Multi-Depth 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_42","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1109\/LGRS.2017.2668299","article-title":"Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network","volume":"14","author":"Palsson","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1109\/JSTARS.2018.2805923","article-title":"Remote Sensing Image Fusion With Deep Convolutional Neural Network","volume":"11","author":"Shao","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_44","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_45","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_46","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_47","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_48","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.inffus.2020.04.006","article-title":"Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion","volume":"62","author":"Ma","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.mechatronics.2015.05.005","article-title":"Towards distributed intelligent sensor and information fusion","volume":"34","author":"Mnks","year":"2016","journal-title":"Mechatronics"},{"key":"ref_50","unstructured":"Liggins, M.E., Hall, D.L., and James, L. (2008). Handbook of Multisensor Data Fusion: Theory and Practice, CRC Press."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/JPROC.1997.554211","article-title":"Distributed fusion architectures and algorithms for target tracking","volume":"85","author":"Liggins","year":"1997","journal-title":"Proc. IEEE"},{"key":"ref_52","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_53","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 8\u201316). Identity Mappings in Deep Residual Networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_54","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":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","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_56","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_57","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.isprsjprs.2013.09.007","article-title":"Bi-cubic interpolation for shift-free pan-sharpening","volume":"86","author":"Aiazzi","year":"2013","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1109\/TGRS.2012.2213604","article-title":"A Sparse Image Fusion Algorithm with Application to Pan-Sharpening","volume":"51","author":"Zhu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","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_61","unstructured":"Yuhas, R.H., Goetz, A.F.H., 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 of the Third Annual JPL Airborne Geoscience Workshop, Pasadena, CA, USA."},{"key":"ref_62","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 Process. Lett."},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.inffus.2019.07.011","article-title":"IFCNN: A general image fusion framework based on convolutional neural network","volume":"54","author":"Zhang","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3192","DOI":"10.1109\/TGRS.2020.3009207","article-title":"Unsupervised Pansharpening Based on Self-Attention Mechanism","volume":"59","author":"Qu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2556\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:23:54Z","timestamp":1760163834000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2556"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,30]]},"references-count":65,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13132556"],"URL":"https:\/\/doi.org\/10.3390\/rs13132556","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,30]]}}}