{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:51:02Z","timestamp":1760403062048,"version":"build-2065373602"},"reference-count":65,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,10]],"date-time":"2021-04-10T00:00:00Z","timestamp":1618012800000},"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>The Visual Place Recognition problem aims to use an image to recognize the location that has been visited before. In most of the scenes revisited, the appearance and view are drastically different. Most previous works focus on the 2-D image-based deep learning method. However, the convolutional features are not robust enough to the challenging scenes mentioned above. In this paper, in order to take advantage of the information that helps the Visual Place Recognition task in these challenging scenes, we propose a new graph construction approach to extract the useful information from an RGB image and a depth image and fuse them in graph data. Then, we deal with the Visual Place Recognition problem as a graph classification problem. We propose a new Global Pooling method\u2014Global Structure Attention Pooling (GSAP), which improves the classification accuracy by improving the expression ability of the Global Pooling component. The experiments show that our GSAP method improves the accuracy of graph classification by approximately 2\u20135%, the graph construction method improves the accuracy of graph classification by approximately 4\u20136%, and that the whole Visual Place Recognition model is robust to appearance change and view change.<\/jats:p>","DOI":"10.3390\/rs13081467","type":"journal-article","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T05:52:00Z","timestamp":1618206720000},"page":"1467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["GSAP: A Global Structure Attention Pooling Method for Graph-Based Visual Place Recognition"],"prefix":"10.3390","volume":"13","author":[{"given":"Yukun","family":"Yang","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Ma","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangdong","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Centre for Autonomous Systems (CAS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shoudong","family":"Huang","sequence":"additional","affiliation":[{"name":"Centre for Autonomous Systems (CAS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TRO.2015.2496823","article-title":"Visual Place Recognition: A Survey","volume":"32","author":"Lowry","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1109\/TRO.2012.2197158","article-title":"Bags of Binary Words for Fast Place Recognition in Image Sequences","volume":"28","author":"Galvezlopez","year":"2012","journal-title":"IEEE Trans. Robot."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mustaqeem, and Kwon, S. (2021). MLT-DNet: Speech emotion recognition using 1D dilated CNN based on multi-learning trick approach. Expert Syst. Appl., 167, 114177.","DOI":"10.1016\/j.eswa.2020.114177"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mustaqeem, and Kwon, S. (2019). A CNN-Assisted Enhanced Audio Signal Processing for Speech Emotion Recognition. Sensors, 20.","DOI":"10.3390\/s20010183"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mustaqeem, and Kwon, S. (2021). Att-Net: Enhanced emotion recognition system using lightweight self-attention module. Appl. Soft Comput., 102, 102.","DOI":"10.1016\/j.asoc.2021.107101"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sunderhauf, N., Shirazi, S., Dayoub, F., Upcroft, B., and Milford, M. (October, January 28). On the performance of ConvNet features for place recognition. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353986"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1109\/TPAMI.2017.2711011","article-title":"NetVLAD: CNN Architecture for Weakly Supervised Place Recognition","volume":"40","author":"Arandjelovic","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, Z., Jacobson, A., Sunderhauf, N., Upcroft, B., Liu, L., Shen, C., Reid, I., and Milford, M. (June, January 29). Deep learning features at scale for visual place recognition. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989366"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Latif, Y., Garg, R., Milford, M., and Reid, I. (2018, January 21\u201325). Addressing Challenging Place Recognition Tasks Using Generative Adversarial Networks. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8461081"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yin, P., Xu, L., Li, X., Yin, C., Li, Y., Srivatsan, R.A., Li, L., Ji, J., and He, Y. (2019, January 20\u201324). A Multi-Domain Feature Learning Method for Visual Place Recognition. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793752"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.neucom.2016.03.029","article-title":"Place recognition based on deep feature and adaptive weighting of similarity matrix","volume":"199","author":"Li","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5723","DOI":"10.1109\/ACCESS.2018.2889030","article-title":"Learning to Fuse Multiscale Features for Visual Place Recognition","volume":"7","author":"Mao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.neucom.2020.02.001","article-title":"Learning Second-order Statistics for Place Recognition based on Robust Covariance Estimation of CNN Features","volume":"398","author":"Zhang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1109\/LRA.2018.2801879","article-title":"X-View: Graph-Based Semantic Multi-View Localization","volume":"3","author":"Gawel","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kong, X., Yang, X., Zhai, G., Zhao, X., Zeng, X., Wang, M., Liu, Y., Li, W., and Wen, F. (2020, January 25\u201329). Semantic Graph Based Place Recognition for 3D Point Clouds. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341060"},{"key":"ref_16","first-page":"1","article-title":"A Comprehensive Survey on Graph Neural Networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_17","unstructured":"Xu, K., Hu, W., Leskovec, J., and Jegelka, S. (2018). How powerful are graph neural networks?. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.1109\/LRA.2020.2969917","article-title":"CoHOG: A Light-Weight, Compute-Efficient, and Training-Free Visual Place Recognition Technique for Changing Environments","volume":"5","author":"Zaffar","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shimoda, S., Ozawa, T., Yamada, K., and Ichitani, Y. (2019). Long-term associative memory in rats: Effects of familiarization period in object-place-context recognition test. bioRxiv, 728295.","DOI":"10.1101\/728295"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Y., Qiu, Y., Cheng, P., and Duan, X. (2020). Robust Loop Closure Detection Integrating Visual\u2013Spatial\u2013Semantic Information via Topological Graphs and CNN Features. Remote Sens., 12.","DOI":"10.3390\/rs12233890"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Garg, S., Suenderhauf, N., and Milford, M. (2018, January 21\u201325). Don\u2019t Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australi.","DOI":"10.1109\/ICRA.2018.8461051"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tsintotas, K.A., Bampis, L., and Gasteratos, A. (2018, January 21\u201325). Assigning Visual Words to Places for Loop Closure Detection. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australi.","DOI":"10.1109\/ICRA.2018.8461146"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Garg, S., Jacobson, A., Kumar, S., and Milford, M. (2017, January 24\u201328). Improving condition- and environment-invariant place recognition with semantic place categorization. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206608"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.robot.2017.03.004","article-title":"Robust visual semi-semantic loop closure detection by a covisibility graph and CNN features","volume":"92","author":"Cascianelli","year":"2017","journal-title":"Robot. Auton. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Garg, S., Suenderhauf, N., and Milford, M. (2019). Semantic-geometric visual place recognition: A new perspective for reconciling opposing views. Int. J. Robot. Res., 0278364919839761.","DOI":"10.1177\/0278364919839761"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Milford, M.J., and Wyeth, G.F. (2012, January 14\u201318). SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights. Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6224623"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Talbot, B., Garg, S., and Milford, M. (2018, January 1\u20135). OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition Under Changing Conditions. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593761"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yue, H., Miao, J., Yu, Y., Chen, W., and Wen, C. (2019, January 3\u20138). Robust Loop Closure Detection based on Bag of SuperPoints and Graph Verification. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967726"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Stumm, E., Mei, C., Lacroix, S., Nieto, J., Hutter, M., and Siegwart, R. (2016, January 27\u201330). Robust Visual Place Recognition with Graph Kernels. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.491"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s11263-014-0774-9","article-title":"Graph-Based Discriminative Learning for Location Recognition","volume":"112","author":"Cao","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sun, Q., Liu, H., He, J., Fan, Z., and Du, X. (2020, January 26\u201329). DAGC: Employing Dual Attention and Graph Convolution for Point Cloud based Place Recognition. Proceedings of the 2020 International Conference on Multimedia Retrieval, Dublin, Ireland.","DOI":"10.1145\/3372278.3390693"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s10846-018-0917-2","article-title":"Graph-Based Place Recognition in Image Sequences with CNN Features","volume":"95","author":"Zhang","year":"2019","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2467","DOI":"10.1016\/j.neucom.2017.11.022","article-title":"Deep convolutional learning for Content Based Image Retrieval","volume":"275","author":"Tzelepi","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_34","first-page":"6154","article-title":"Discriminative Deep Quantization Hashing for Face Image Retrieval","volume":"29","author":"Tang","year":"2018","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.neucom.2018.04.034","article-title":"Optimization of deep convolutional neural network for large scale image retrieval","volume":"303","author":"Bai","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_36","first-page":"5264","article-title":"Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval","volume":"29","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/TPAMI.2018.2846566","article-title":"Fine-Tuning CNN Image Retrieval with No Human Annotation","volume":"41","author":"Radenovic","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","unstructured":"Song, J. (2017, January 21\u201326). Binary Generative Adversarial Networks for Image Retrieval. Proceedings of the Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.neucom.2017.05.099","article-title":"Cross-modal subspace learning for fine-grained sketch-based image retrieval","volume":"278","author":"Xu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Pang, K., Li, K., Yang, Y., Zhang, H., Hospedales, T.M., Xiang, T., and Song, Y.Z. (2019, January 15\u201320). Generalising Fine-Grained Sketch-Based Image Retrieval. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00077"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Dutta, A., and Akata, Z. (2019, January 15\u201320). Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-Based Image Retrieval. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00523"},{"key":"ref_42","unstructured":"Guo, X., Wu, H., Cheng, Y., Rennie, S.J., Tesauro, G., and Feris, R.S. (2018). Dialog-based Interactive Image Retrieval. arXiv."},{"key":"ref_43","unstructured":"Kipf, T., and Welling, M. (2016). Semi-Supervised Classification with Graph Convolutional Networks. arXiv."},{"key":"ref_44","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph Attention Networks. arXiv."},{"key":"ref_45","unstructured":"Hamilton, W.L., Ying, Z., and Leskovec, J. (2021, February 16). Inductive Representation Learning on Large Graphs. Available online: https:\/\/arxiv.org\/abs\/1706.02216."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Schlichtkrull, M.S., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., and Welling, M. (2018, January 3\u20137). Modeling Relational Data with Graph Convolutional Networks. Proceedings of the 15th International Conference on Extended Semantic Web Conference, ESWC 2018, Heraklion, Crete, Greece.","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"ref_47","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., and Dahl, G.E. (2017, January 6\u201311). Neural Message Passing for Quantum Chemistry. Proceedings of the 34th International Conference on Machine Learning-Volume 70, Sydney, Australia."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R.B., Gupta, A., and He, K. (2017). Non-local Neural Networks. arXiv.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_49","unstructured":"Battaglia, P.W., Hamrick, J.B., Bapst, V., Sanchezgonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., and Faulkner, R. (2018). Relational inductive biases, deep learning, and graph networks. arXiv."},{"key":"ref_50","unstructured":"Cangea, C., Veli\u010dkovi\u0107, P., Jovanovi\u0107, N., Kipf, T., and Li\u00f2, P. (2018). Towards Sparse Hierarchical Graph Classifiers. arXiv."},{"key":"ref_51","unstructured":"Knyazev, B., Taylor, G.W., and Amer, M.R. (2019). Understanding attention in graph neural networks. arXiv."},{"key":"ref_52","unstructured":"Lee, J., Lee, I., and Kang, J. (2019, January 10\u201315). Self-Attention Graph Pooling. Proceedings of the 36th International Conference on Machine Learning, ICML 2019, Beach, CA, USA."},{"key":"ref_53","unstructured":"Diehl, F. (2019). Edge contraction pooling for graph neural networks. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ranjan, E., Sanyal, S., and Talukdar, P. (2020, January 7\u201312). ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i04.5997"},{"key":"ref_55","unstructured":"Vinyals, O., Bengio, S., and Kudlur, M. (2016, January 2\u20134). Order Matters: Sequence to sequence for sets. Proceedings of the ICLR 2016: International Conference on Learning Representations 2016, San Juan, Puerto Rico."},{"key":"ref_56","unstructured":"Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R.S. (2016). Gated Graph Sequence Neural Networks. ICLR (Poster). arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhang, M., Cui, Z., Neumann, M., and Chen, Y. (2018, January 2\u20137). An End-to-End Deep Learning Architecture for Graph Classification. Proceedings of the AAAI Conference on Artificial Intelligence AAAI, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mustaqeem, and Kwon, S. (2020). CLSTM: Deep Feature-Based Speech Emotion Recognition Using the Hierarchical ConvLSTM Network. Mathematics, 8.","DOI":"10.3390\/math8122133"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Mustaqeem, Sajjad, M., and Kwon, S. (2020). Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM. IEEE Access, 8, 79861\u201379875.","DOI":"10.1109\/ACCESS.2020.2990405"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014, January 25\u201329). Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_61","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_62","unstructured":"Mesquita, D.P.P., Souza, A.H., and Kaski, S. (2021, February 16). Rethinking Pooling in Graph Neural Networks. Available online: https:\/\/arxiv.org\/pdf\/2010.11418.pdf."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Prince, D.S.J.D. (2012). Computer Vision: Models, Learning, and Inference, Cambridge University Press.","DOI":"10.1017\/CBO9780511996504"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., and Lopez, A.M. (2016, January 27\u201330). The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.352"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Valada, A., Vertens, J., Dhall, A., and Burgard, W. (June, January 29). AdapNet: Adaptive semantic segmentation in adverse environmental conditions. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989540"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:27:43Z","timestamp":1760365663000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,10]]},"references-count":65,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081467"],"URL":"https:\/\/doi.org\/10.3390\/rs13081467","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,4,10]]}}}