{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:37:27Z","timestamp":1775839047165,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,7]],"date-time":"2017-11-07T00:00:00Z","timestamp":1510012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["3102016ZY019, 3102016ZB012"],"award-info":[{"award-number":["3102016ZY019, 3102016ZB012"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671383"],"award-info":[{"award-number":["61671383"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Defense Basic Scientific Research Project","award":["JCKY2016203C067"],"award-info":[{"award-number":["JCKY2016203C067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral images are well-known for their fine spectral resolution to discriminate different materials. However, their spatial resolution is relatively low due to the trade-off in imaging sensor technologies, resulting in limitations in their applications. Inspired by recent achievements in convolutional neural network (CNN) based super-resolution (SR) for natural images, a novel three-dimensional full CNN (3D-FCNN) is constructed for spatial SR of hyperspectral images in this paper. Specifically, 3D convolution is used to exploit both the spatial context of neighboring pixels and spectral correlation of neighboring bands, such that spectral distortion when directly applying traditional CNN based SR algorithms to hyperspectral images in band-wise manners is alleviated. Furthermore, a sensor-specific mode is designed for the proposed 3D-FCNN such that none of the samples from the target scene are required for training. Fine-tuning by a small number of training samples from the target scene can further improve the performance of such a sensor-specific method. Extensive experimental results on four benchmark datasets from two well-known hyperspectral sensors, namely hyperspectral digital imagery collection experiment (HYDICE) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed 3D-FCNN outperforms several existing SR methods by ensuring higher quality both in reconstruction and spectral fidelity.<\/jats:p>","DOI":"10.3390\/rs9111139","type":"journal-article","created":{"date-parts":[[2017,11,7]],"date-time":"2017-11-07T11:46:01Z","timestamp":1510055161000},"page":"1139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":301,"title":["Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8018-596X","authenticated-orcid":false,"given":"Shaohui","family":"Mei","sequence":"first","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Xin","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Jingyu","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Yifan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Shuai","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Qian","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1109\/TIP.2004.829779","article-title":"Map estimation for hyperspectral image resolution enhancement using an auxiliary sensor","volume":"13","author":"Hardie","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1924","DOI":"10.1109\/TGRS.2004.830644","article-title":"Application of the stochastic mixing model to hyperspectral resolution enhancement using auxiliary sensor","volume":"42","author":"Eismann","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1109\/TGRS.2004.837324","article-title":"Hyperspectral resolution enhancement using high-resolution multispectral imagery with arbitrary response functions","volume":"43","author":"Eismann","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/TGRS.2011.2161320","article-title":"Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion","volume":"50","author":"Yokoya","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6574","DOI":"10.1109\/TGRS.2014.2298056","article-title":"Hyperspectral image resolution enhancement using high-resolution multispectral image based on spectral unmixing","volume":"52","author":"Bendoumi","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","first-page":"201","article-title":"Spatial resolution enhancement of hyperspectral image based on the combination of spectral mixing model and observation model","volume":"9244","author":"Zhang","year":"2014","journal-title":"Proc. SPIE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.rse.2017.05.011","article-title":"Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps","volume":"196","author":"Li","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/79.974727","article-title":"Spectral unmixing","volume":"19","author":"Keshava","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1109\/TGRS.2002.802494","article-title":"Spatial\/spectral endmember extraction by multidimensional morphological operations","volume":"40","author":"Plaza","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3434","DOI":"10.1109\/TGRS.2010.2046671","article-title":"Spatial Purity Based Endmember Extraction for Spectral Mixture Analysis","volume":"48","author":"Mei","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1109\/LGRS.2009.2039114","article-title":"Mixture Analysis by Multichannel Hopfield Neural Network","volume":"7","author":"Mei","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2590","DOI":"10.1109\/TGRS.2009.2038483","article-title":"Minimum Dispersion Constrained Nonnegative Matrix Factorization to Unmix Hyperspectral Data","volume":"48","author":"Huck","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4282","DOI":"10.1109\/TGRS.2011.2144605","article-title":"Hyperspectral Unmixing via L1\/2 Sparsity-Constrained Nonnegative Matrix Factorization","volume":"49","author":"Qian","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2744","DOI":"10.1109\/TGRS.2011.2174443","article-title":"A New Minimum-Volume Enclosing Algorithm for Endmember Identification and Abundance Estimation in Hyperspectral Data","volume":"50","author":"Hendrix","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1109\/JSTARS.2013.2281414","article-title":"Unsupervised Spectral Mixture Analysis of Highly Mixed Data with Hopfield Neural Network","volume":"7","author":"Mei","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1109\/36.934072","article-title":"Hyperspectral subpixel target detection using the linear mixing model","volume":"39","author":"Manolakis","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4955","DOI":"10.1109\/TGRS.2013.2286195","article-title":"Hyperspectral Remote Sensing Image Subpixel Target Detection Based on Supervised Metric Learning","volume":"52","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","unstructured":"Gu, Y., Liu, Y., and Zhang, Y. (2007, January 5\u20137). A Soft Classification Algorithm based on Spectral-spatial Kernels in Hyperspectral Images. Proceedings of the IEEE International Conference on Innovative Computing, Information and Control, Kumamoto, Japan."},{"key":"ref_19","first-page":"166","article-title":"Mapping subpixel boundaries from remotely sensed images","volume":"Volume 4","author":"Atkinson","year":"1997","journal-title":"Innovations in GIS"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1080\/014311698214659","article-title":"Sharpening fuzzy classification output to refine the representation of sub-pixel land cover distribution","volume":"19","author":"Foody","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/S0034-4257(01)00242-5","article-title":"Land cover mapping at sub-pixel scales using linear optimization techniques","volume":"79","author":"Verhoeye","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3293","DOI":"10.1080\/01431160500497127","article-title":"A sub-pixel mapping algorithm based on sub-pixel\/pixel spatial attraction models","volume":"27","author":"Mertens","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"839","DOI":"10.14358\/PERS.71.7.839","article-title":"Sub-pixel target mapping from soft-classified, remotely sensed imagery","volume":"71","author":"Atkinson","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_24","first-page":"1089","article-title":"Bayesian image super-resolution, continued","volume":"19","author":"Pickup","year":"2006","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/JSTARS.2012.2227246","article-title":"Sub-pixel mapping based on a MAP model with multiple shifted hyperspectral imagery","volume":"6","author":"Xu","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.rse.2005.02.006","article-title":"Super-resolution land cover mapping using a Markov random field based approach","volume":"96","author":"Kasetkasem","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1109\/LGRS.2012.2215573","article-title":"Subpixel mapping using Markov random field with multiple spectral constraints from subpixel shifted remote sensing images","volume":"10","author":"Wang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5023","DOI":"10.1080\/01431160903252350","article-title":"Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images","volume":"31","author":"Ling","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2004.03.003","article-title":"Subpixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients","volume":"91","author":"Mertens","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(01)00229-2","article-title":"Super-resolution land cover pattern prediction using a Hopfield neural network","volume":"79","author":"Tatem","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1109\/JSTSP.2010.2096798","article-title":"Spectral unmixing for the classification of hyperspectral images at a finer spatial resolution","volume":"5","author":"Villa","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1109\/TGRS.2015.2506612","article-title":"Adaptive sparse subpixel mapping with a total variation model for remote sensing imagery","volume":"54","author":"Feng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1109\/JSTARS.2014.2305652","article-title":"Example-based super-resolution land cover mapping using support vector regression","volume":"7","author":"Zhang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Xue, X., Wang, T., and He, M. (2017). A Hybrid Subpixel Mapping Framework for Hyperspectral Images Using Collaborative Representation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.","DOI":"10.1109\/JSTARS.2017.2732227"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Villa, A., Chanussot, J., Benediktsson, J.A., Ulfarsson, M., and Jutten, C. (2010, January 25\u201330). Super-resolution: An efficient method to improve spatial resolution of hyperspectral image. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5654208"},{"key":"ref_36","unstructured":"Dong, W.S., Zhang, L., Shi, G.M., and Wu, X. (2009, January 7\u201310). Nonlocal Back-Projection for Adaptive Image Enlargement. Proceedings of the 2009 16th IEEE International Conference on Image Processing (ICIP), Cairo, Egypt."},{"key":"ref_37","first-page":"121","article-title":"Two Stage Interpolation Algorithm Based on Fuzzy Logics and Edges Features for Image Zooming","volume":"1","author":"Chen","year":"2009","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","article-title":"Image super-resolution via sparse representation","volume":"19","author":"Yang","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3194","DOI":"10.1109\/TIP.2012.2190080","article-title":"Image super-resolution with sparse neighbor embedding","volume":"21","author":"Gao","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2014). Learning a Deep Convolutional Network for Image Super-Resolution. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (2016, January 27\u201330). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27\u201330). Accurate Image Super-Resolution Using Very Deep Convolutional Networks. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"883","DOI":"10.5194\/isprs-archives-XLI-B3-883-2016","article-title":"Single-Image Super Resolution for Multispectral Remote Sensing Data Using Convolutional Neural Networks","volume":"XLI-B3","author":"Liebel","year":"2016","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.neucom.2017.05.024","article-title":"Hyperspectral image super-resolution using deep convolutional neural network","volume":"266","author":"Li","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1109\/LGRS.2017.2737637","article-title":"Hyperspectral Image Super-Resolution by Spectral Difference Learning and Spatial Error Correction","volume":"14","author":"Hu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","unstructured":"Galliani, S., Lanaras, C., Marmanis, D., Baltsavias, E., and Schindler, K. (arXiv, 2017). Learned Spectral Super-Resolution, arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, S., Jiao, L., and Yang, S. (2016). Hierarchical sparse learning with spectral-spatial information for hyperspectral imagery denoising. Sensors, 16.","DOI":"10.3390\/s16101718"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5425","DOI":"10.1109\/TGRS.2016.2564639","article-title":"Noise Removal from Hyperspectral Image with Joint Spectral-Spatial Distributed Sparse Representation","volume":"54","author":"Li","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2012.2197589","article-title":"Advances in Spectral-Spatial Classification of Hyperspectral Images","volume":"101","author":"Fauvel","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1109\/LGRS.2017.2751559","article-title":"Locality Adaptive Discriminant Analysis for Spectral-Spatial Classification of Hyperspectral Images","volume":"11","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1109\/LGRS.2016.2579661","article-title":"Hyperspectral Image Super-Resolution by Spectral Mixture Analysis and Spatial-Spectral Group Sparsity","volume":"13","author":"Li","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., and Doulamis, N. (2015, January 26\u201331). Deep supervised learning for hyperspectral data classification through convolutional neural networks. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D Convolutional Neural Networks for Human Action Recognition","volume":"35","author":"Ji","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_55","unstructured":"Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J. (2015, January 7\u201312). 3D ShapeNets: A deep representation for volumetric shapes. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Su, H., Niebner, M., Dai, A., Yan, M., and Guibas, L.J. (2016, January 27\u201330). Volumetric and Multi-view CNNs for Object Classification on 3D Data. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.609"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhao, Y., Yi, C., and Chan, J.-W. (2017). No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning. Remote Sens., 9.","DOI":"10.3390\/rs9040305"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/11\/1139\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:32Z","timestamp":1760208512000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/11\/1139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,7]]},"references-count":59,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2017,11]]}},"alternative-id":["rs9111139"],"URL":"https:\/\/doi.org\/10.3390\/rs9111139","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,11,7]]}}}