{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:47:18Z","timestamp":1760240838621,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T00:00:00Z","timestamp":1569456000000},"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":["U1605254, 41471379, 61371144"],"award-info":[{"award-number":["U1605254, 41471379, 61371144"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Establishing the spatial relationship between 2D images captured by real cameras and 3D models of the environment (2D and 3D space) is one way to achieve the virtual\u2013real registration for Augmented Reality (AR) in outdoor environments. In this paper, we propose to match the 2D images captured by real cameras and the rendered images from the 3D image-based point cloud to indirectly establish the spatial relationship between 2D and 3D space. We call these two kinds of images as cross-domain images, because their imaging mechanisms and nature are quite different. However, unlike real camera images, the rendered images from the 3D image-based point cloud are inevitably contaminated with image distortion, blurred resolution, and obstructions, which makes image matching with the handcrafted descriptors or existing feature learning neural networks very challenging. Thus, we first propose a novel end-to-end network, AE-GAN-Net, consisting of two AutoEncoders (AEs) with Generative Adversarial Network (GAN) embedding, to learn invariant feature descriptors for cross-domain image matching. Second, a domain-consistent loss function, which balances image content and consistency of feature descriptors for cross-domain image pairs, is introduced to optimize AE-GAN-Net. AE-GAN-Net effectively captures domain-specific information, which is embedded into the learned feature descriptors, thus making the learned feature descriptors robust against image distortion, variations in viewpoints, spatial resolutions, rotation, and scaling. Experimental results show that AE-GAN-Net achieves state-of-the-art performance for image patch retrieval with the cross-domain image patch dataset, which is built from real camera images and the rendered images from 3D image-based point cloud. Finally, by evaluating virtual\u2013real registration for AR on a campus by using the cross-domain image matching results, we demonstrate the feasibility of applying the proposed virtual\u2013real registration to AR in outdoor environments.<\/jats:p>","DOI":"10.3390\/rs11192243","type":"journal-article","created":{"date-parts":[[2019,9,27]],"date-time":"2019-09-27T03:03:15Z","timestamp":1569553395000},"page":"2243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["AE-GAN-Net: Learning Invariant Feature Descriptor to Match Ground Camera Images and a Large-Scale 3D Image-Based Point Cloud for Outdoor Augmented Reality"],"prefix":"10.3390","volume":"11","author":[{"given":"Weiquan","family":"Liu","sequence":"first","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6075-796X","authenticated-orcid":false,"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China"}]},{"given":"Xuesheng","family":"Bian","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China"}]},{"given":"Shuting","family":"Chen","sequence":"additional","affiliation":[{"name":"Information Engineering School, Chengyi University College, Jimei University, Xiamen 361021, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China"}]},{"given":"Xiuhong","family":"Lin","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China"}]},{"given":"Yongchuan","family":"Li","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China"}]},{"given":"Dongdong","family":"Weng","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Shang-Hong","family":"Lai","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan"}]},{"given":"Jonathan","family":"Li","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China"},{"name":"Department of Geography and Environmental Management University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.edurev.2016.11.002","article-title":"Advantages and challenges associated with augmented reality for education: A systematic review of the literature","volume":"20","year":"2017","journal-title":"Educ. Res. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rao, J., Qiao, Y., Ren, F., Wang, J., and Du, Q. (2017). A mobile outdoor augmented reality method combining deep learning object detection and spatial relationships for geovisualization. Sensors, 17.","DOI":"10.3390\/s17091951"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.isprsjprs.2009.10.001","article-title":"Augmented reality and photogrammetry: A synergy to visualize physical and virtual city environments","volume":"65","author":"Lerma","year":"2010","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Luchetti, G., Mancini, A., Sturari, M., Frontoni, E., and Zingaretti, P. (2017). Whistland: An augmented reality crowd-mapping system for civil protection and emergency management. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6020041"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.eswa.2016.01.037","article-title":"A 3D GIS-based interactive registration mechanism for outdoor augmented reality system","volume":"55","author":"Huang","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pellas, N., Fotaris, P., Kazanidis, I., and Wells, D. (2018). Augmenting the learning experience in primary and secondary school education: A systematic review of recent trends in augmented reality game-based learning. Virtual Reality, Springer.","DOI":"10.1007\/s10055-018-0347-2"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, P., Liu, X., Cheng, W., and Huang, R. (2017). A review of using Augmented Reality in Education from 2011 to 2016. Innovations in Smart Learning, Springer.","DOI":"10.1007\/978-981-10-2419-1_2"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.media.2017.01.007","article-title":"The status of augmented reality in laparoscopic surgery as of 2016","volume":"37","author":"Bernhardt","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1038\/s41377-018-0006-0","article-title":"A fiber optoacoustic guide with augmented reality for precision breast-conserving surgery","volume":"7","author":"Lan","year":"2018","journal-title":"Light Sci. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jocn.2016.09.002","article-title":"Utilizing virtual and augmented reality for educational and clinical enhancements in neurosurgery","volume":"35","author":"Pelargos","year":"2017","journal-title":"J. Clin. Neurosci."},{"key":"ref_11","unstructured":"Pang, Y., Yuan, M., Nee, A.Y., Ong, S.K., and Youcef-Toumi, K. (2006, January 16\u201319). A markerless registration method for augmented reality based on affine properties. Proceedings of the 7th Australasian User Interface Conference\u2014Volume 50, Hobart, Australia."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1016\/j.cag.2005.09.014","article-title":"A generalized registration method for augmented reality systems","volume":"29","author":"Yuan","year":"2005","journal-title":"Comput. Graph."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Panou, C., Ragia, L., Dimelli, D., and Mania, K. (2018). An Architecture for Mobile Outdoors Augmented Reality for Cultural Heritage. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7120463"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/38.963459","article-title":"Recent advances in augmented reality","volume":"21","author":"Azuma","year":"2001","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_15","unstructured":"Schonberger, J.L., and Frahm, J.M. (July, January 26). Structure-from-motion revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jensen, J., and Mathews, A. (2016). Assessment of image-based point cloud products to generate a bare earth surface and estimate canopy heights in a woodland ecosystem. Remote. Sens., 8.","DOI":"10.3390\/rs8010050"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"12037","DOI":"10.3390\/rs61212037","article-title":"Feature learning based approach for weed classification using high resolution aerial images from a digital camera mounted on a UAV","volume":"6","author":"Hung","year":"2014","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Van Gool, L. (2006, January 7\u201313). Surf: Speeded up robust features. Proceedings of the European Conference on Computer Vision, Graz, Austria.","DOI":"10.1007\/11744023_32"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1109\/TPAMI.2009.77","article-title":"Daisy: An efficient dense descriptor applied to wide-baseline stereo","volume":"32","author":"Tola","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G.R. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the IEEE International Conference on Computer Vision. Citeseer, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., and Moreno-Noguer, F. (2015, January 7\u201313). Discriminative learning of deep convolutional feature point descriptors. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.22"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tian, Y., Fan, B., and Wu, F. (2017, January 21\u201326). L2-net: Deep learning of discriminative patch descriptor in euclidean space. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.649"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yang, T.Y., Hsu, J.H., Lin, Y.Y., and Chuang, Y.Y. (2017, January 22\u201329). Deepcd: Learning deep complementary descriptors for patch representations. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.359"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, W., Shen, X., Wang, C., Zhang, Z., Wen, C., and Li, J. (2018, January 13\u201319). H-Net: Neural Network for Cross-domain Image Patch Matching. Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI), Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/119"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_27","unstructured":"Kumar, B., Carneiro, G., and Reid, I. (2016, January 27\u201330). Learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, K., Lu, Y., and Sclaroff, S. (2018, January 18\u201323). Local descriptors optimized for average precision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00069"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Keller, M., Chen, Z., Maffra, F., Schmuck, P., and Chli, M. (2018, January 18\u201323). Learning deep descriptors with scale-aware triplet networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00292"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dong, Y., Jiao, W., Long, T., Liu, L., He, G., Gong, C., and Guo, Y. (2019). Local Deep Descriptor for Remote Sensing Image Feature Matching. Remote Sens., 11.","DOI":"10.3390\/rs11040430"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lenc, K., and Vedaldi, A. (2016, January 8\u201316). Learning covariant feature detectors. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-49409-8_11"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.inffus.2017.12.007","article-title":"A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines","volume":"44","author":"Charte","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_33","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tayara, H., Ham, W., and Chong, K. (2016). A real-time marker-based visual sensor based on a FPGA and a soft core processor. Sensors, 16.","DOI":"10.3390\/s16122139"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2288","DOI":"10.1109\/TVCG.2016.2617325","article-title":"Augmented reality marker hiding with texture deformation","volume":"23","author":"Kawai","year":"2016","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TVCG.2017.2745941","article-title":"The hologram in my hand: How effective is interactive exploration of 3D visualizations in immersive tangible augmented reality?","volume":"24","author":"Bach","year":"2017","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"31092","DOI":"10.3390\/s151229847","article-title":"Sensor-aware recognition and tracking for wide-area augmented reality on mobile phones","volume":"15","author":"Chen","year":"2015","journal-title":"Sensors"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yi, K.M., Trulls, E., Lepetit, V., and Fua, P. (2016, January 8\u201316). Lift: Learned invariant feature transform. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46466-4_28"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Cui, Y., Belongie, S., and Hays, J. (2015, January 7\u201312). Learning deep representations for ground-to-aerial geolocalization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299135"},{"key":"ref_40","unstructured":"Melekhov, I., Kannala, J., and Rahtu, E. (2016, January 20\u201324). Image patch matching using convolutional descriptors with euclidean distance. Proceedings of the Asian Conference on Computer Vision, Taipei, Taiwan."},{"key":"ref_41","unstructured":"Bromley, J., Guyon, I., LeCun, Y., S\u00e4ckinger, E., and Shah, R. (December, January 28). Signature verification using a \u201csiamese\u201d time delay neural network. Proceedings of the Advances in Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_42","unstructured":"Chopra, S., Hadsell, R., and LeCun, Y. (2005, January 20\u201326). Learning a similarity metric discriminatively, with application to face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_43","unstructured":"Han, X., Leung, T., Jia, Y., Sukthankar, R., and Berg, A.C. (2015, January 7\u201312). Matchnet: Unifying feature and metric learning for patch-based matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., and Komodakis, N. (2015, January 7\u201312). Learning to compare image patches via convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299064"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TPAMI.2010.54","article-title":"Discriminative learning of local image descriptors","volume":"33","author":"Brown","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1109\/TPAMI.2005.188","article-title":"A performance evaluation of local descriptors","volume":"27","author":"Krystian","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Balntas, V., Lenc, K., Vedaldi, A., and Mikolajczyk, K. (2017, January 21\u201326). HPatches: A benchmark and evaluation of handcrafted and learned local descriptors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.410"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hu, Y., Gibson, E., Vercauteren, T., Ahmed, H., Emberton, M., Moore, C., Noble, J., and Barratt, D. (2017, January 11\u201313). Intraoperative organ motion models with an ensemble of conditional generative adversarial networks. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-66185-8_42"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., and Catanzaro, B. (2018, January 18\u201323). High-resolution image synthesis and semantic manipulation with conditional gans. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00917"},{"key":"ref_50","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_51","unstructured":"Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S. (2017, January 4\u20139). Self-normalizing neural networks. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","unstructured":"Nie, D., Trullo, R., Lian, J., Petitjean, C., Ruan, S., Wang, Q., and Shen, D. (2017, January 11\u201313). Medical image synthesis with context-aware generative adversarial networks. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-66179-7_48"},{"key":"ref_54","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_55","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_56","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2243\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:24:41Z","timestamp":1760189081000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2243"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,26]]},"references-count":57,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["rs11192243"],"URL":"https:\/\/doi.org\/10.3390\/rs11192243","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,9,26]]}}}