{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:08:23Z","timestamp":1775470103779,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,11]],"date-time":"2021-09-11T00:00:00Z","timestamp":1631318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007406","name":"Fundaci\u00f3n BBVA","doi-asserted-by":"publisher","award":["2021\/00203\/00"],"award-info":[{"award-number":["2021\/00203\/00"]}],"id":[{"id":"10.13039\/100007406","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Nowadays, a large number of remote sensing instruments are providing a massive amount of data within the frame of different Earth Observation missions. These instruments are characterized by the wide variety of data they can collect, as well as the impressive volume of data and the speed at which it is acquired. In this sense, hyperspectral imaging data has certain properties that make it difficult to process, such as its large spectral dimension coupled with problematic data variability. To overcome these challenges, convolutional neural networks have been proposed as classification models because of their ability to extract relevant spectral\u2013spatial features and learn hidden patterns, along their great architectural flexibility. Their high performance relies on the convolution kernels to exploit the spatial relationships. Thus, filter design is crucial for the correct performance of models. Nevertheless, hyperspectral data may contain objects with different shapes and orientations, preventing filters from \u201cseeing everything possible\u201d during the decision making. To overcome this limitation, this paper proposes a novel adaptable convolution model based on deforming kernels combined with deforming convolution layers to fit their effective receptive field to the input data. The proposed adaptable convolutional network (named DKDCNet) has been evaluated over two well-known hyperspectral scenes, demonstrating that it is able to achieve better results than traditional strategies with similar computational cost for HSI classification.<\/jats:p>","DOI":"10.3390\/rs13183637","type":"journal-article","created":{"date-parts":[[2021,9,12]],"date-time":"2021-09-12T21:48:01Z","timestamp":1631483281000},"page":"3637","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Adaptable Convolutional Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1030-3729","authenticated-orcid":false,"given":"Mercedes E.","family":"Paoletti","sequence":"first","affiliation":[{"name":"Department of Computer Architecture and Automation, Faculty of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6701-961X","authenticated-orcid":false,"given":"Juan M.","family":"Haut","sequence":"additional","affiliation":[{"name":"Hyperspectral Computing Laboratory (HyperComp), Department of Computer Technology and Communications, Escuela Politecnica, University of Extremadura, 10002 Caceres, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Srivastava, P.K., Malhi, R.K.M., Pandey, P.C., Anand, A., Singh, P., Pandey, M.K., and Gupta, A. (2020). Revisiting hyperspectral remote sensing: Origin, processing, applications and way forward. Hyperspectral Remote Sensing, Elsevier.","DOI":"10.1016\/B978-0-08-102894-0.00001-2"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.isprsjprs.2017.11.021","article-title":"A new deep convolutional neural network for fast hyperspectral image classification","volume":"145","author":"Paoletti","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1109\/36.934082","article-title":"Automated differentiation of urban surfaces based on airborne hyperspectral imagery","volume":"39","author":"Roessner","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shobiga, R., and Selvakumar, J. (2015, January 27). Survey on properties and accuracy assessment of climate changes using hyperspectral imaging. Proceedings of the 2015 Online International Conference on Green Engineering and Technologies (IC-GET), Coimbatore, India.","DOI":"10.1109\/GET.2015.7453851"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","article-title":"When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.rse.2018.08.024","article-title":"Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images","volume":"217","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tao, X., Paoletti, M.E., Haut, J.M., Ren, P., Plaza, J., and Plaza, A. (2021). Endmember Estimation with Maximum Distance Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13040713"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tao, X., Paoletti, M.E., Haut, J.M., Hang, L., Ren, P., Plaza, J., and Plaza, A. (2021). Endmember Estimation from Hyperspectral Images Using Geometric Distances. IEEE Geosci. Remote Sens. Lett.","DOI":"10.1109\/LGRS.2021.3102076"},{"key":"ref_10","unstructured":"Simonetti, E., Simonetti, D., and Preatoni, D. (2014). Phenology-Based Land Cover Classification Using Landsat 8 Time Series, European Commission Joint Research Center."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Boonprong, S., Cao, C., Chen, W., Ni, X., Xu, M., and Acharya, B.K. (2018). The classification of noise-afflicted remotely sensed data using three machine-learning techniques: Effect of different levels and types of noise on accuracy. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7070274"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4817234","DOI":"10.1155\/2020\/4817234","article-title":"Overview of hyperspectral image classification","volume":"2020","author":"Lv","year":"2020","journal-title":"J. Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1007\/s11227-016-1896-3","article-title":"Cloud implementation of the K-means algorithm for hyperspectral image analysis","volume":"73","author":"Haut","year":"2017","journal-title":"J. Supercomput."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, T., Zhang, J., and Zhang, Y. (2014, January 27\u201330). Classification of hyperspectral image based on deep belief networks. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7026039"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1109\/LGRS.2018.2872358","article-title":"Boltzmann entropy-based unsupervised band selection for hyperspectral image classification","volume":"16","author":"Gao","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1108\/SR-07-2016-0124","article-title":"Survey of supervised classification techniques for hyperspectral images","volume":"37","author":"Qiu","year":"2017","journal-title":"Sens. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6712","DOI":"10.1109\/TGRS.2018.2841823","article-title":"Exploring hierarchical convolutional features for hyperspectral image classification","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, G., and Ren, P. (2020). Hyperspectral Image Classification with Feature-Oriented Adversarial Active Learning. Remote Sens., 12.","DOI":"10.3390\/rs12233879"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Alcolea, A., Paoletti, M.E., Haut, J.M., Resano, J., and Plaza, A. (2020). Inference in supervised spectral classifiers for on-board hyperspectral imaging: An overview. Remote Sens., 12.","DOI":"10.3390\/rs12030534"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bruzzone, L., Chi, M., and Marconcini, M. (2007). Semisupervised support vector machines for classification of hyperspectral remote sensing images. Hyperspectral Data Exploitation: Theory and Applications, John Wiley & Sons.","DOI":"10.1002\/9780470124628.ch11"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2014.08.003","article-title":"An efficient semi-supervised classification approach for hyperspectral imagery","volume":"97","author":"Tan","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6440","DOI":"10.1109\/TGRS.2018.2838665","article-title":"Active learning with convolutional neural networks for hyperspectral image classification using a new bayesian approach","volume":"56","author":"Haut","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4810","DOI":"10.1109\/TGRS.2015.2410991","article-title":"Region-kernel-based support vector machines for hyperspectral image classification","volume":"53","author":"Peng","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Paoletti, M.E., Haut, J.M., Tao, X., Miguel, J.P., and Plaza, A. (2020). A new GPU implementation of support vector machines for fast hyperspectral image classification. Remote Sens., 12.","DOI":"10.3390\/rs12081257"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1109\/LGRS.2014.2320258","article-title":"A subspace-based multinomial logistic regression for hyperspectral image classification","volume":"11","author":"Khodadadzadeh","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1109\/JMASS.2020.3019669","article-title":"Cloud Implementation of Multinomial Logistic Regression for UAV Hyperspectral Images","volume":"1","author":"Haut","year":"2020","journal-title":"IEEE J. Miniaturization Air Space Syst."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/JSTARS.2018.2809781","article-title":"Cascaded random forest for hyperspectral image classification","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1109\/JSTARS.2013.2251969","article-title":"Combining multiple classification methods for hyperspectral data interpretation","volume":"6","author":"Santos","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/MGRS.2019.2912563","article-title":"Deep learning for classification of hyperspectral data: A comparative review","volume":"7","author":"Audebert","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2019.09.006","article-title":"Deep learning classifiers for hyperspectral imaging: A review","volume":"158","author":"Paoletti","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1111\/tgis.12164","article-title":"Hyperspectral remote sensing classifications: A perspective survey","volume":"20","author":"Chutia","year":"2016","journal-title":"Trans. GIS"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1109\/TGRS.2017.2765364","article-title":"Recent advances on spectral\u2013spatial hyperspectral image classification: An overview and new guidelines","volume":"56","author":"He","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6547","DOI":"10.1109\/TGRS.2017.2729882","article-title":"Multiple kernel learning for hyperspectral image classification: A review","volume":"55","author":"Gu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3804","DOI":"10.1109\/TGRS.2008.922034","article-title":"Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles","volume":"46","author":"Fauvel","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/LGRS.2012.2191761","article-title":"Discriminative Gabor feature selection for hyperspectral image classification","volume":"10","author":"Shen","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1080\/01431161.2015.1007251","article-title":"Improving hyperspectral image classification by combining spectral, texture, and shape features","volume":"36","author":"Mirzapour","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_39","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. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2016.09.010","article-title":"Convolutional neural networks for hyperspectral image classification","volume":"219","author":"Yu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liang, H., and Li, Q. (2016). Hyperspectral imagery classification using sparse representations of convolutional neural network features. Remote Sens., 8.","DOI":"10.3390\/rs8020099"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral\u2013spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ding, C., Li, Y., Xia, Y., Wei, W., Zhang, L., and Zhang, Y. (2017). Convolutional neural networks based hyperspectral image classification method with adaptive kernels. Remote Sens., 9.","DOI":"10.3390\/rs9060618"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, W., Dou, S., Jiang, Z., and Sun, L. (2018). A fast dense spectral\u2013spatial convolution network framework for hyperspectral images classification. Remote Sens., 10.","DOI":"10.3390\/rs10071068"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1109\/LGRS.2018.2881045","article-title":"Low\u2013high-power consumption architectures for deep-learning models applied to hyperspectral image classification","volume":"16","author":"Haut","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.patcog.2016.10.019","article-title":"Hyperspectral image reconstruction by deep convolutional neural network for classification","volume":"63","author":"Li","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Gao, Q., Lim, S., and Jia, X. (2018). Hyperspectral image classification using convolutional neural networks and multiple feature learning. Remote Sens., 10.","DOI":"10.3390\/rs10020299"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.3390\/rs10091454","article-title":"Deep&dense convolutional neural network for hyperspectral image classification","volume":"10","author":"Paoletti","year":"2018","journal-title":"Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fang, B., Li, Y., Zhang, H., and Chan, J.C.W. (2019). Hyperspectral images classification based on dense convolutional networks with spectral-wise attention mechanism. Remote Sens., 11.","DOI":"10.3390\/rs11020159"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral\u2013spatial 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_51","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TGRS.2018.2860125","article-title":"Deep pyramidal residual networks for spectral\u2013spatial hyperspectral image classification","volume":"57","author":"Paoletti","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/LGRS.2018.2873476","article-title":"Dual-path network-based hyperspectral image classification","volume":"16","author":"Kang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1109\/TGRS.2018.2871782","article-title":"Capsule networks for hyperspectral image classification","volume":"57","author":"Paoletti","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ding, X., Li, Y., Yang, J., Li, H., Liu, L., Liu, Y., and Zhang, C. (2021). An adaptive capsule network for hyperspectral remote sensing classification. Remote Sens., 13.","DOI":"10.3390\/rs13132445"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1109\/LGRS.2019.2909495","article-title":"Hyperspectral image classification using random occlusion data augmentation","volume":"16","author":"Haut","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"9201","DOI":"10.1109\/TGRS.2019.2925615","article-title":"Multi-Scale Dense Networks for Hyperspectral Remote Sensing Image Classification","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/LGRS.2018.2830403","article-title":"Deformable convolutional neural networks for hyperspectral image classification","volume":"15","author":"Zhu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_58","unstructured":"Luo, W., Li, Y., Urtasun, R., and Zemel, R. (2016, January 5\u201310). Understanding the effective receptive field in deep convolutional neural networks. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_59","unstructured":"Gao, H., Zhu, X., Lin, S., and Dai, J. (2019). Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_61","unstructured":"Le, H., and Borji, A. (2017). What are the receptive, effective receptive, and projective fields of neurons in convolutional neural networks?. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1113\/jphysiol.1962.sp006837","article-title":"Receptive fields, binocular interaction and functional architecture in the cat\u2019s visual cortex","volume":"160","author":"Hubel","year":"1962","journal-title":"J. Physiol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1038\/333452a0","article-title":"Network model of shape-from-shading: Neural function arises from both receptive and projective fields","volume":"333","author":"Lehky","year":"1988","journal-title":"Nature"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e21","DOI":"10.23915\/distill.00021","article-title":"Computing receptive fields of convolutional neural networks","volume":"4","author":"Araujo","year":"2019","journal-title":"Distill"},{"key":"ref_65","unstructured":"Seeley, J., and Bowyer, S. (1988). ROSIS (Reflective Optics System Imaging Spectrometer)\u2014A candidate instrument for polar platform missions. SPIE 0868 Optoelectronic Technologies for Remote Sensing from Space, International Society for Optics and Photonics."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Xu, X., Li, J., and Plaza, A. (2016, January 10\u201315). Fusion of hyperspectral and LiDAR data using morphological component analysis. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729926"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"5277","DOI":"10.1109\/TGRS.2019.2961681","article-title":"Lightweight spectral-spatial squeeze-and-excitation residual bag-of-features learning for hyperspectral classification","volume":"58","author":"Roy","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3637\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:01:07Z","timestamp":1760166067000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3637"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,11]]},"references-count":67,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183637"],"URL":"https:\/\/doi.org\/10.3390\/rs13183637","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,11]]}}}