{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:03:41Z","timestamp":1787029421230,"version":"3.56.0"},"reference-count":59,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,4,3]],"date-time":"2020-04-03T00:00:00Z","timestamp":1585872000000},"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":["61871306"],"award-info":[{"award-number":["61871306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772400"],"award-info":[{"award-number":["61772400"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773304"],"award-info":[{"award-number":["61773304"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2019JM-194"],"award-info":[{"award-number":["2019JM-194"]}]},{"name":"Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences","award":["LSIT201803D"],"award-info":[{"award-number":["LSIT201803D"]}]},{"name":"Joint Fund of the Equipment Research of Ministry of Education","award":["6141A020337"],"award-info":[{"award-number":["6141A020337"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Classifying hyperspectral images (HSIs) with limited samples is a challenging issue. The generative adversarial network (GAN) is a promising technique to mitigate the small sample size problem. GAN can generate samples by the competition between a generator and a discriminator. However, it is difficult to generate high-quality samples for HSIs with complex spatial\u2013spectral distribution, which may further degrade the performance of the discriminator. To address this problem, a symmetric convolutional GAN based on collaborative learning and attention mechanism (CA-GAN) is proposed. In CA-GAN, the generator and the discriminator not only compete but also collaborate. The shallow to deep features of real multiclass samples in the discriminator assist the sample generation in the generator. In the generator, a joint spatial\u2013spectral hard attention module is devised by defining a dynamic activation function based on a multi-branch convolutional network. It impels the distribution of generated samples to approximate the distribution of real HSIs both in spectral and spatial dimensions, and it discards misleading and confounding information. In the discriminator, a convolutional LSTM layer is merged to extract spatial contextual features and capture long-term spectral dependencies simultaneously. Finally, the classification performance of the discriminator is improved by enforcing competitive and collaborative learning between the discriminator and generator. Experiments on HSI datasets show that CA-GAN obtains satisfactory classification results compared with advanced methods, especially when the number of training samples is limited.<\/jats:p>","DOI":"10.3390\/rs12071149","type":"journal-article","created":{"date-parts":[[2020,4,7]],"date-time":"2020-04-07T03:58:39Z","timestamp":1586231919000},"page":"1149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":100,"title":["Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"12","author":[{"given":"Jie","family":"Feng","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueliang","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiantong","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianghai","family":"Cao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0379-2042","authenticated-orcid":false,"given":"Xiangrong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Licheng","family":"Jiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences, Beijing 100864, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chang, C.I. (2007). Hyperspectral Data Exploitation: Theory and Applications, Wiley.","DOI":"10.1002\/0470124628"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.isprsjprs.2016.12.009","article-title":"A survey of landmine detection using hyperspectral imaging","volume":"124","author":"Makki","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1109\/JSTARS.2015.2406339","article-title":"Generation of spectral-temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications","volume":"8","author":"Gevaert","year":"2015","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1080\/08120090500134530","article-title":"Hyperspectral imaging spectroscopy of a Mars analogue environment at the North Pole Dome, Pilbara Craton, Western Australia","volume":"52","author":"Brown","year":"2005","journal-title":"Austral. J. Earth Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7140","DOI":"10.1109\/TGRS.2017.2743102","article-title":"PCA-based edge-preserving features for hyperspectral image classification","volume":"55","author":"Kang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1109\/TGRS.2016.2645703","article-title":"Dimensionality reduction and classification of hyperspectral images using ensemble discriminative local metric learning","volume":"55","author":"Dong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1109\/JSTARS.2016.2606578","article-title":"Dimensionality reduction of hyperspectral imagery using sparse graph learning","volume":"10","author":"Chen","year":"2017","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3235","DOI":"10.1109\/TGRS.2015.2514161","article-title":"Nonlinear multiple kernel learning with multiple-structure-element extended morphological profiles for hyperspectral image classification","volume":"54","author":"Gu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/TGRS.2014.2318332","article-title":"Spectral-spatial classification of hyperspectral data via morphological component analysis-based image separation","volume":"53","author":"Xue","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1109\/JSTARS.2015.2423278","article-title":"Spectral-Spatial Hyperspectral Image Classification Using Regularized Low-Rank Representation and Sparse Representation-Based Graph Cuts","volume":"8","author":"Jia","year":"2015","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6663","DOI":"10.1109\/TGRS.2015.2445767","article-title":"Classification of hyperspectral images by exploiting spectral-spatial information of superpixel via multiple kernels","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4186","DOI":"10.1109\/TGRS.2015.2392755","article-title":"Spectral-spatial classification of hyperspectral images with a superpixel-based discriminative sparse model","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2462","DOI":"10.1109\/JSTARS.2013.2252150","article-title":"Spatial-spectral kernel sparse representation for hyperspectral image classification","volume":"6","author":"Liu","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3973","DOI":"10.1109\/TGRS.2011.2129595","article-title":"Hyperspectral image classification using dictionary-based sparse representation","volume":"49","author":"Chen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","first-page":"222","article-title":"Heathland conservation status mapping through integration of hyperspectral mixture analysis and decision tree classifiers","volume":"126","author":"Delalieux","year":"2012","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","unstructured":"Gualtieriand, J.A., and Chettri, S. (2000, January 24\u201328). Support vector machines for classification of hyperspectral data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhong, S., Chang, C.I., and Zhang, Y. (2018, January 7\u201310). Iterative Support Vector Machine for Hyperspectral Image Classification. Proceedings of the 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451145"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TGRS.2004.842481","article-title":"Investigation of the random forest framework for classification of hyperspectral data","volume":"43","author":"Ham","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.neucom.2015.02.023","article-title":"Laplacian auto-encoders: An explicit learning of nonlinear data manifold","volume":"160","author":"Jia","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2015.11.044","article-title":"Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging","volume":"185","author":"Zabalza","year":"2016","journal-title":"Neurocomputer"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1109\/TGRS.2019.2893180","article-title":"Learning compact and discriminative stacked autoencoder for hyperspectral image classification","volume":"57","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral-spatial classification of hyperspectral data based on deep belief network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3516","DOI":"10.1109\/TGRS.2017.2675902","article-title":"Learning to diversify deep belief networks for hyperspectral image classification","volume":"55","author":"Zhong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ghassemi, M., Ghassemian, H., and Imani, M. (2018, January 20\u201321). Deep Belief Networks for Feature Fusion in Hyperspectral Image Classification. Proceedings of the IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES), Bali, Indonesia.","DOI":"10.1109\/ICARES.2018.8547136"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"183","DOI":"10.26599\/TST.2018.9010043","article-title":"Multiple deep-belief-network-based spectral-spatial classification of hyperspectral images","volume":"24","author":"Mughees","year":"2018","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1080\/2150704X.2017.1280200","article-title":"Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network","volume":"8","author":"Zhang","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep feature extraction and classification of hyperspectral images based on convolutional neural networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wu, H., and Prasad, S. (2017). Convolutional recurrent neural networks for hyperspectral data classification. Remote Sens., 9.","DOI":"10.3390\/rs9030298"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.1109\/TGRS.2018.2794326","article-title":"Hyperspectral image classification with deep feature fusion network","volume":"56","author":"Song","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","article-title":"Going deeper with contextual CNN for hyperspectral image classification","volume":"26","author":"Lee","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral-spatial residual network for hyperspectral image classification: A 3-D deep learning framework","volume":"56","author":"Zhong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2016.2616355","article-title":"Hyperspectral image classification using deep pixel-pair features","volume":"55","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_36","unstructured":"Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., and Lee, H. (2016, January 19\u201324). Generative adversarial text-to-image synthesis. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_37","unstructured":"Mathieu, M., Couprie, C., and LeCun, Y. (2016, January 2\u20134). Deep multi-scale video prediction beyond mean square error. Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_39","unstructured":"Che, T., Li, Y., Zhang, R., Hjelm, R.D., Li, W., Song, Y., and Bengio, Y. (2017). Maximum-likelihood augmented discrete generative adversarial networks. arXiv."},{"key":"ref_40","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_41","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the 34 th International Conference on Machine Learning (ICML), Sydney, Australia."},{"key":"ref_42","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017, January 4\u20139). Improved training of Wasserstein GANs. Proceedings of the Advances in Neural Information Processing Systems. (NIPS), Long Beach, CA, USA."},{"key":"ref_43","unstructured":"Zhao, J., Mathieu, M., and LeCun, Y. (2017, January 24\u201326). Energy-based generative adversarial network. Proceedings of the International Conference on Learning Representations. (ICLR), Toulon, France."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, D., Vinson, R., Holmes, M., Seibel, G., Bechar, A., Nof, S., and Tao, Y. (2018). Early Tomato Spotted Wilt Virus Detection using Hyperspectral Imaging Technique and Outlier Removal Auxiliary Classifier Generative Adversarial Nets (OR-AC-GAN). 2018 ASABE Annual International Meeting, American Society of Agricultural and Biological Engineers.","DOI":"10.13031\/aim.201800660"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1109\/LGRS.2018.2890413","article-title":"SiftingGAN: Generating and sifting labeled samples to improve the remote sensing image scene classification baseline in vitro","volume":"16","author":"Ma","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","unstructured":"Denton, E., Chintala, S., Szlam, A., and Fergus, R. (2015). Deep generative image models using a laplacian pyramid of adversarial networks. arXiv."},{"key":"ref_47","unstructured":"Radford, A., Metz, L., and Chintala, S. (2016, January 20). Unsupervised representation learning with deep convolutional generative adversarial networks. Proceedings of the International Conference on Learning Representations ICLR, Toulon, France."},{"key":"ref_48","unstructured":"Durugkar, I., Gemp, I., and Mahadevan, S. (2017, January 24\u201326). Generative multi-adversarial networks. Proceedings of the International Conference on Learning Representations. (ICLR), Toulon, France."},{"key":"ref_49","unstructured":"Neyshabur, B., Bhojanapalli, S., and Chakrabarti, A. (2017). Stabilizing GAN Training With Multiple Random Projections. arXiv."},{"key":"ref_50","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016, January 5\u201310). Improved techniques for training GANs. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_51","unstructured":"Zhong, Z., Li, J., Clausi, D.A., and Wong, A. (2019). Generative adversarial networks and conditional random fields for hyperspectral image classification. IEEE Trans. Cybernetics, 1\u201312."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, Z., Liu, H., Wang, Y., and Hu, J. (2017). Generative adversarial networks-based semi-supervised learning for hyperspectral image classification. Remote Sens., 9.","DOI":"10.3390\/rs9101042"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/LGRS.2017.2780890","article-title":"Semisupervised hyperspectral image classification based on generative adversarial networks","volume":"15","author":"Zhan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhan, Y., Wu, K., Liu, W., Qin, J., Yang, Z., Medjadba, Y., and Yu, X. (2018, January 22\u201327). Semi-supervised classification of hyperspectral data based on generative adversarial networks and neighborhood majority voting. Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518846"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhan, Y., Qin, J., Huang, T., Wu, K., Hu, D., Zhao, Z., and Wang, G. (August, January 28). Hyperspectral Image Classification Based on Generative Adversarial Networks with Feature Fusing and Dynamic Neighborhood Voting Mechanism. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899291"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Gao, H., Yao, D., Wang, M., Li, C., Liu, H., Hua, Z., and Wang, J. (2019). A Hyperspectral Image Classification Method Based on Multi-Discriminator Generative Adversarial Networks. Sensors, 19.","DOI":"10.3390\/s19153269"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5046","DOI":"10.1109\/TGRS.2018.2805286","article-title":"Generative adversarial networks for hyperspectral image classification","volume":"56","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5329","DOI":"10.1109\/TGRS.2019.2899057","article-title":"Classification of Hyperspectral Images Based on Multiclass Spatial-Spectral Generative Adversarial Networks","volume":"57","author":"Feng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015, January 7\u201312). Convolutional lstm network: A machine learning approach for precipitation nowcasting. Proceedings of the Advances in Neural Information Processing Systems: Annual Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:15:08Z","timestamp":1760174108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,3]]},"references-count":59,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12071149"],"URL":"https:\/\/doi.org\/10.3390\/rs12071149","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,3]]}}}