{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:51:34Z","timestamp":1776181894865,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100002418","name":"Intel Corporation","doi-asserted-by":"publisher","award":["SRA 10-18-17"],"award-info":[{"award-number":["SRA 10-18-17"]}],"id":[{"id":"10.13039\/100002418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OIA-1757351"],"award-info":[{"award-number":["OIA-1757351"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000199","name":"U.S. Department of Agriculture","doi-asserted-by":"publisher","award":["NR213A750013G021"],"award-info":[{"award-number":["NR213A750013G021"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral imaging systems are becoming widely used due to their increasing accessibility and their ability to provide detailed spectral responses based on hundreds of spectral bands. However, the resulting hyperspectral images (HSIs) come at the cost of increased storage requirements, increased computational time to process, and highly redundant data. Thus, dimensionality reduction techniques are necessary to decrease the number of spectral bands while retaining the most useful information. Our contribution is two-fold: First, we propose a filter-based method called interband redundancy analysis (IBRA) based on a collinearity analysis between a band and its neighbors. This analysis helps to remove redundant bands and dramatically reduces the search space. Second, we apply a wrapper-based approach called greedy spectral selection (GSS) to the results of IBRA to select bands based on their information entropy values and train a compact convolutional neural network to evaluate the performance of the current selection. We also propose a feature extraction framework that consists of two main steps: first, it reduces the total number of bands using IBRA; then, it can use any feature extraction method to obtain the desired number of feature channels. We present classification results obtained from our methods and compare them to other dimensionality reduction methods on three hyperspectral image datasets. Additionally, we used the original hyperspectral data cube to simulate the process of using actual filters in a multispectral imager.<\/jats:p>","DOI":"10.3390\/rs13183649","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T23:32:23Z","timestamp":1631575943000},"page":"3649","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy Analysis and Greedy Spectral Selection"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-2911-8558","authenticated-orcid":false,"given":"Giorgio","family":"Morales","sequence":"first","affiliation":[{"name":"Gianforte School of Computing, Montana State University, Bozeman, MT 59717, USA"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-9487-5622","authenticated-orcid":false,"given":"John W.","family":"Sheppard","sequence":"additional","affiliation":[{"name":"Gianforte School of Computing, Montana State University, Bozeman, MT 59717, USA"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-5258-5472","authenticated-orcid":false,"given":"Riley D.","family":"Logan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Montana State University, Bozeman, MT 59717, USA"},{"name":"Optical Technology Center, Montana State University, Bozeman, MT 59717, USA"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-1056-1269","authenticated-orcid":false,"given":"Joseph A.","family":"Shaw","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Montana State University, Bozeman, MT 59717, USA"},{"name":"Optical Technology Center, Montana State University, Bozeman, MT 59717, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"044516","DOI":"10.1117\/1.JRS.13.044516","article-title":"Hyperspectral imaging and neural networks to classify herbicide-resistant weeds","volume":"13","author":"Scherrer","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/BF03374530","article-title":"Remote sensing applications in forensic investigations","volume":"35","author":"Davenport","year":"2001","journal-title":"Hist. Archaeol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"F71","DOI":"10.1364\/AO.47.000F71","article-title":"Design and fabrication of a low-cost, multispectral imaging system","volume":"47","author":"Mathews","year":"2008","journal-title":"Appl. Opt."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.compag.2012.07.003","article-title":"A high-resolution airborne four-camera imaging system for agricultural remote sensing","volume":"88","author":"Yang","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ifset.2013.04.014","article-title":"Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review\u2014Part I: Fundamentals","volume":"19","author":"Wu","year":"2013","journal-title":"Innov. Food Sci. Emerg. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1016\/j.rse.2007.12.014","article-title":"Three decades of hyperspectral remote sensing of the Earth: A personal view","volume":"113","author":"Goetz","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_7","unstructured":"Hook, S.J. (2014). NASA 2014 the Hyperspectral Infrared Imager (HyspIRI)\u2013Science Impact of Deploying Instruments on Separate Platforms, Jet Propulsion Laboratory, California Institute of Technology. White Paper 14-13."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Guo, X., Zhang, H., Wu, Z., Zhao, J., and Zhang, Z. (2017). Comparison and Evaluation of Annual NDVI Time Series in China Derived from the NOAA AVHRR LTDR and Terra MODIS MOD13C1 Products. Sensors, 17.","DOI":"10.3390\/s17061298"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"47","DOI":"10.15446\/dyna.v85n205.69516","article-title":"Normalized difference vegetation index for rice management in El Espinal, Colombia","volume":"85","year":"2018","journal-title":"DYNA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4158","DOI":"10.1109\/TGRS.2007.904951","article-title":"Clustering-Based Hyperspectral Band Selection Using Information Measures","volume":"45","author":"Pla","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","unstructured":"Ghojogh, B., Samad, M.N., Mashhadi, S.A., Kapoor, T., Ali, W., Karray, F., and Crowley, M. (2019). Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2018.09.008","article-title":"UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras","volume":"146","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Vasefi, F., MacKinnon, N., and Farkas, D. (2016). Hyperspectral and Multispectral Imaging in Dermatology. Imaging in Dermatology, Academic Press.","DOI":"10.1016\/B978-0-12-802838-4.00016-9"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Morales, G., Sheppard, J., Logan, R., and Shaw, J. (2021, January 18\u201322). Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN2021), virtual event.","DOI":"10.1109\/IJCNN52387.2021.9533700"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bellman, R. (1961). Adaptive Control Processes: A Guided Tour, Princeton University Press.","DOI":"10.1515\/9781400874668"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/MGRS.2019.2911100","article-title":"Hyperspectral Band Selection: A Review","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Uddin, M.P., Mamun, M.A., and Hossain, M.A. (2017, January 21\u201323). Feature extraction for hyperspectral image classification. Proceedings of the IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, Bangladesh.","DOI":"10.1109\/R10-HTC.2017.8288979"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/LGRS.2008.2001282","article-title":"Limitations of Principal Components Analysis for Hyperspectral Target Recognition","volume":"5","author":"Prasad","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jayaprakash, C., Damodaran, B.B., Sowmya, V., and Soman, K.P. (2018, January 22\u201323). Dimensionality Reduction of Hyperspectral Images for Classification using Randomized Independent Component Analysis. Proceedings of the 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India.","DOI":"10.1109\/SPIN.2018.8474266"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Du, Q., and Younan, N. (2008). Dimensionality Reduction and Linear Discriminant Analysis for Hyperspectral Image Classification. Knowledge-Based Intelligent Information and Engineering Systems, Springer.","DOI":"10.1007\/978-3-540-85567-5_49"},{"key":"ref_22","first-page":"1027","article-title":"Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis","volume":"8","author":"Sugiyama","year":"2007","journal-title":"J. Mach. Learn. Res."},{"key":"ref_23","first-page":"181","article-title":"Partial Least Squares Discriminant Analysis: A Dimensionality Reduction Method to Classify Hyperspectral Data","volume":"31","author":"Fordellone","year":"2019","journal-title":"Stat. Appl.-Ital. J. Appl. Stat."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2960","DOI":"10.1109\/JSTARS.2017.2682189","article-title":"Learning a Robust Local Manifold Representation for Hyperspectral Dimensionality Reduction","volume":"10","author":"Hong","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhuang, L., Gao, L., Zhang, B., Fu, X., and Bioucas-Dias, J.M. (2020). Hyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations. IEEE Trans. Geosci. Remote Sens., 1\u201317.","DOI":"10.1109\/TGRS.2020.3040221"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5028","DOI":"10.1109\/TGRS.2020.3011002","article-title":"A Fast Neighborhood Grouping Method for Hyperspectral Band Selection","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","first-page":"5910","article-title":"Optimal Clustering Framework for Hyperspectral Band Selection","volume":"56","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","first-page":"6018769","article-title":"Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology","volume":"2017","author":"Li","year":"2017","journal-title":"Int. J. Anal. Chem."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Walton, N., Sheppard, J., and Shaw, J. (2019, January 13\u201317). Using a Genetic Algorithm with Histogram-Based Feature Selection in Hyperspectral Image Classification. Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO\u201919), Prague, Czech Republic.","DOI":"10.1145\/3321707.3321748"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"18580","DOI":"10.1038\/s41598-019-54987-1","article-title":"An Efficient hybrid filter-wrapper metaheuristic-based gene selection method for high dimensional datasets","volume":"9","author":"Pirgazi","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Taherkhani, F., Dawson, J., and Nasrabadi, N. (2019). Deep Sparse Band Selection for Hyperspectral Face Recognition. arXiv.","DOI":"10.1007\/978-3-030-38617-7_11"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tschannerl, J., Ren, J., Zabalza, J., and Marshall, S. (2018, January 26\u201328). Segmented Autoencoders for Unsupervised Embedded Hyperspectral Band Selection. Proceedings of the 2018 7th European Workshop on Visual Information Processing (EUVIP), Tampere, Finland.","DOI":"10.1109\/EUVIP.2018.8611643"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1109\/36.803411","article-title":"A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification","volume":"37","author":"Chang","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1039\/an9952002787","article-title":"Wavelength selection method for multicomponent spectrophotometric determinations using partial least squares","volume":"120","author":"Frenich","year":"1995","journal-title":"Analyst"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.ifset.2012.12.011","article-title":"NIR hyperspectral imaging as non-destructive evaluation tool for the recognition of fresh and frozen\u2013thawed porcine longissimus dorsi muscles","volume":"18","author":"Barbin","year":"2013","journal-title":"Innov. Food Sci. Emerg. Technol."},{"key":"ref_36","unstructured":"Preet, P., and Batra, S.S. (2015). Feature Selection for classification of hyperspectral data by minimizing a tight bound on the VC dimension. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1137\/1116025","article-title":"On the Uniform Convergence of Relative Frequencies of Events to their Probabilities","volume":"16","author":"Vapnik","year":"1971","journal-title":"Theory Probab. Its Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4317","DOI":"10.1080\/01431161.2016.1212423","article-title":"A data fusion and spatial data analysis approach for the estimation of wheat grain nitrogen uptake from satellite data","volume":"37","author":"Castaldi","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1126\/science.1242072","article-title":"Clustering by fast search and find of density peaks","volume":"344","author":"Rodriguez","year":"2014","journal-title":"Science"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Xu, B., Li, X., Hou, W., Wang, Y., and Wei, Y. (2021). A Similarity-Based Ranking Method for Hyperspectral Band Selection. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2020.3048138"},{"key":"ref_41","first-page":"413","article-title":"Band selection based on optimization approach for hyperspectral image classification","volume":"21","author":"Medjahed","year":"2018","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2009.05.011","article-title":"Hyperspectral waveband selection for on-line measurement of grain cleanness","volume":"104","author":"Wallays","year":"2009","journal-title":"Biosyst. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1186\/s13007-019-0476-y","article-title":"Hyperspectral imaging for seed quality and safety inspection: A review","volume":"15","author":"Feng","year":"2019","journal-title":"Plant Methods"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Fang, B., Li, Y., Zhang, H., and Chan, J. (2019). Hyperspectral Images Classification Based on Dense Convolutional Networks with Spectral-Wise Attention Mechanism. Remote Sens., 11.","DOI":"10.3390\/rs11020159"},{"key":"ref_45","unstructured":"Gao, X., Zhao, Y., Dudziak, L., Mullins, R., and Xu, C.Z. (2019, January 6\u20139). Dynamic channel pruning: Feature boosting and suppression. Proceedings of the 7th International Conference on Learning Representations (ICLR), New Orleans, LA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6268","DOI":"10.1038\/s41598-019-42557-4","article-title":"Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization","volume":"9","author":"Pasa","year":"2019","journal-title":"Sci. Rep. Vis."},{"key":"ref_47","unstructured":"Pan, J., Ferrer, C., McGuinness, K., O\u2019Connor, N., Torres, J., Sayrol, E., and Giro-i-Nieto, X. (2017). SalGAN: Visual Saliency Prediction with Generative Adversarial Networks. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s13007-019-0479-8","article-title":"Plant disease identification using explainable 3D deep learning on hyperspectral images","volume":"15","author":"Nagasubramanian","year":"2019","journal-title":"Plant Methods"},{"key":"ref_49","unstructured":"Baumgardner, M.F., Biehl, L.L., and Landgrebe, D.A. (2015). 220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3, Purdue University Research Repository."},{"key":"ref_50","unstructured":"Gualtieri, A.G., Chettri, S., Cromp, R.F., and Johnson, L.F. (1999, January 9\u201311). Support Vector Machine Classifiers as Applied to AVIRIS Data. Proceedings of the Summaries of the Eighth JPL Airborne Earth Science Workshop, Pasadena, CA, USA."},{"key":"ref_51","first-page":"35","article-title":"Survey of Weed Control and Production Practices on Sugarbeet in Eastern North Dakota and Minnesota-2000","volume":"32","author":"Dexter","year":"2001","journal-title":"Sugarbeet Res. Ext. Rep"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1614\/0043-1745(2002)050[0498:GDOK]2.0.CO;2","article-title":"Genetic diversity of Kochia","volume":"50","author":"Mengistu","year":"2002","journal-title":"Weed Sci."},{"key":"ref_53","unstructured":"Waite, J.C. (2010). Glyphosate Resistance in Kochia (Kochia scoparia). [Master\u2019s Thesis, Department of Agronomy, College of Agriculture, Kansas State University]."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1117\/12.942280","article-title":"A System Overview of the Airborne Visible\/Infrared Imaging Spectrometer (AVIRIS)","volume":"Volume 0834","author":"Vane","year":"1987","journal-title":"Imaging Spectroscopy II"},{"key":"ref_55","unstructured":"Tadjudin, S. (1998). Classification of High Dimensional Data with Limited Training Samples. [Ph.D. Thesis, School of Electrical and Computer Engineering, Purdue University]."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R, Springer Science + Business Media.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Belsley, D., Kuh, E., Welsch, R., and Wells, R. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, Wiley.","DOI":"10.1002\/0471725153"},{"key":"ref_58","unstructured":"Hair, J., Anderson, R., and Babin, B. (2009). Multivariate Data Analysis, Prentice Hall. [7th ed.]."},{"key":"ref_59","first-page":"036519","article-title":"Reduced-Cost Hyperspectral Convolutional Neural Networks","volume":"14","author":"Morales","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_61","unstructured":"Howard, A., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_62","unstructured":"Zeiler, M. (2012). ADADELTA: An Adaptive Learning Rate Method. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1002\/cem.785","article-title":"Partial Least Squares for Discrimination","volume":"17","author":"Marker","year":"2003","journal-title":"J. Chemom."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/2072-4292\/13\/18\/3649\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:01:34Z","timestamp":1760166094000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/2072-4292\/13\/18\/3649"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,13]]},"references-count":63,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183649"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/rs13183649","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,13]]}}}