{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:20:09Z","timestamp":1773246009697,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T00:00:00Z","timestamp":1654214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["41871316"],"award-info":[{"award-number":["41871316"]}]},{"name":"National Nature Science Foundation of China","award":["41401457"],"award-info":[{"award-number":["41401457"]}]},{"name":"National Nature Science Foundation of China","award":["2020GGJS028"],"award-info":[{"award-number":["2020GGJS028"]}]},{"name":"National Nature Science Foundation of China","award":["202300410096"],"award-info":[{"award-number":["202300410096"]}]},{"name":"National Nature Science Foundation of China","award":["202016511"],"award-info":[{"award-number":["202016511"]}]},{"name":"National Nature Science Foundation of China","award":["21A170008"],"award-info":[{"award-number":["21A170008"]}]},{"name":"National Nature Science Foundation of China","award":["2003009"],"award-info":[{"award-number":["2003009"]}]},{"name":"Young Key Teacher Training Plan of Henan","award":["41871316"],"award-info":[{"award-number":["41871316"]}]},{"name":"Young Key Teacher Training Plan of Henan","award":["41401457"],"award-info":[{"award-number":["41401457"]}]},{"name":"Young Key Teacher Training Plan of Henan","award":["2020GGJS028"],"award-info":[{"award-number":["2020GGJS028"]}]},{"name":"Young Key Teacher Training Plan of Henan","award":["202300410096"],"award-info":[{"award-number":["202300410096"]}]},{"name":"Young Key Teacher Training Plan of Henan","award":["202016511"],"award-info":[{"award-number":["202016511"]}]},{"name":"Young Key Teacher Training Plan of Henan","award":["21A170008"],"award-info":[{"award-number":["21A170008"]}]},{"name":"Young Key Teacher Training Plan of Henan","award":["2003009"],"award-info":[{"award-number":["2003009"]}]},{"name":"Natural Science Foundation of Henan","award":["41871316"],"award-info":[{"award-number":["41871316"]}]},{"name":"Natural Science Foundation of Henan","award":["41401457"],"award-info":[{"award-number":["41401457"]}]},{"name":"Natural Science Foundation of Henan","award":["2020GGJS028"],"award-info":[{"award-number":["2020GGJS028"]}]},{"name":"Natural Science Foundation of Henan","award":["202300410096"],"award-info":[{"award-number":["202300410096"]}]},{"name":"Natural Science Foundation of Henan","award":["202016511"],"award-info":[{"award-number":["202016511"]}]},{"name":"Natural Science Foundation of Henan","award":["21A170008"],"award-info":[{"award-number":["21A170008"]}]},{"name":"Natural Science Foundation of Henan","award":["2003009"],"award-info":[{"award-number":["2003009"]}]},{"name":"Natural Resources Science and Technology Innovation Project of Henan Province","award":["41871316"],"award-info":[{"award-number":["41871316"]}]},{"name":"Natural Resources Science and Technology Innovation Project of Henan Province","award":["41401457"],"award-info":[{"award-number":["41401457"]}]},{"name":"Natural Resources Science and Technology Innovation Project of Henan Province","award":["2020GGJS028"],"award-info":[{"award-number":["2020GGJS028"]}]},{"name":"Natural Resources Science and Technology Innovation Project of Henan Province","award":["202300410096"],"award-info":[{"award-number":["202300410096"]}]},{"name":"Natural Resources Science and Technology Innovation Project of Henan Province","award":["202016511"],"award-info":[{"award-number":["202016511"]}]},{"name":"Natural Resources Science and Technology Innovation Project of Henan Province","award":["21A170008"],"award-info":[{"award-number":["21A170008"]}]},{"name":"Natural Resources Science and Technology Innovation Project of Henan Province","award":["2003009"],"award-info":[{"award-number":["2003009"]}]},{"name":"Key Scientific Research Project Plans of Higher Education Institutions of Henan","award":["41871316"],"award-info":[{"award-number":["41871316"]}]},{"name":"Key Scientific Research Project Plans of Higher Education Institutions of Henan","award":["41401457"],"award-info":[{"award-number":["41401457"]}]},{"name":"Key Scientific Research Project Plans of Higher Education Institutions of Henan","award":["2020GGJS028"],"award-info":[{"award-number":["2020GGJS028"]}]},{"name":"Key Scientific Research Project Plans of Higher Education Institutions of Henan","award":["202300410096"],"award-info":[{"award-number":["202300410096"]}]},{"name":"Key Scientific Research Project Plans of Higher Education Institutions of Henan","award":["202016511"],"award-info":[{"award-number":["202016511"]}]},{"name":"Key Scientific Research Project Plans of Higher Education Institutions of Henan","award":["21A170008"],"award-info":[{"award-number":["21A170008"]}]},{"name":"Key Scientific Research Project Plans of Higher Education Institutions of Henan","award":["2003009"],"award-info":[{"award-number":["2003009"]}]},{"name":"Technology Development Plan Project of Kaifeng","award":["41871316"],"award-info":[{"award-number":["41871316"]}]},{"name":"Technology Development Plan Project of Kaifeng","award":["41401457"],"award-info":[{"award-number":["41401457"]}]},{"name":"Technology Development Plan Project of Kaifeng","award":["2020GGJS028"],"award-info":[{"award-number":["2020GGJS028"]}]},{"name":"Technology Development Plan Project of Kaifeng","award":["202300410096"],"award-info":[{"award-number":["202300410096"]}]},{"name":"Technology Development Plan Project of Kaifeng","award":["202016511"],"award-info":[{"award-number":["202016511"]}]},{"name":"Technology Development Plan Project of Kaifeng","award":["21A170008"],"award-info":[{"award-number":["21A170008"]}]},{"name":"Technology Development Plan Project of Kaifeng","award":["2003009"],"award-info":[{"award-number":["2003009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land use is used to reflect the expression of human activities in space, and land use classification is a way to obtain accurate land use information. Obtaining high-precision land use classification from remote sensing images remains a significant challenge. Traditional machine learning methods and image semantic segmentation models are unable to make full use of the spatial and contextual information of images. This results in land use classification that does not meet high-precision requirements. In order to improve the accuracy of land use classification, we propose a land use classification model, called DADNet-CRFs, that integrates an attention mechanism and conditional random fields (CRFs). The model is divided into two modules: the Dual Attention Dense Network (DADNet) and CRFs. First, the convolution method in the UNet network is modified to Dense Convolution, and the band-hole pyramid pooling module, spatial location attention mechanism module, and channel attention mechanism module are fused at appropriate locations in the network, which together form DADNet. Second, the DADNet segmentation results are used as a priori conditions to guide the training of CRFs. The model is tested with the GID dataset, and the results show that the overall accuracy of land use classification obtained with this model is 7.36% and 1.61% higher than FCN-8s and BiSeNet in classification accuracy, 11.95% and 1.81% higher in MIoU accuracy, and with a 9.35% and 2.07% higher kappa coefficient, respectively. The proposed DADNet-CRFs model can fully use the spatial and contextual semantic information of high-resolution remote sensing images, and it effectively improves the accuracy of land use classification. The model can serve as a highly accurate automatic classification tool for land use classification and mapping high-resolution images.<\/jats:p>","DOI":"10.3390\/rs14112688","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T10:33:01Z","timestamp":1654252381000},"page":"2688","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Land Use Classification Model Based on Conditional Random Fields and Attention Mechanism Convolutional Networks"],"prefix":"10.3390","volume":"14","author":[{"given":"Kang","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Environment and Planning, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng 475004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6679-9543","authenticated-orcid":false,"given":"Haiying","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Environment and Planning, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng 475004, China"},{"name":"Henan Technology Innovation Center of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China"},{"name":"Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China"}]},{"given":"Fen","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Environment and Planning, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng 475004, China"},{"name":"Henan Technology Innovation Center of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China"},{"name":"Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9993-3382","authenticated-orcid":false,"given":"Zhigang","family":"Han","sequence":"additional","affiliation":[{"name":"College of Environment and Planning, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng 475004, China"},{"name":"Henan Technology Innovation Center of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China"},{"name":"Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1080\/13658816.2017.1324976","article-title":"Classifying Urban Land Use by Integrating Remote Sensing and Social Media Data","volume":"31","author":"Liu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1080\/19475683.2014.992369","article-title":"Change Analysis of Land Use\/Land Cover and Modelling Urban Growth in Greater Doha, Qatar","volume":"21","author":"Hashem","year":"2015","journal-title":"Ann. GIS"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1016\/j.mex.2019.03.023","article-title":"Mapping Global Eco-Environment Vulnerability Due to Human and Nature Disturbances","volume":"6","author":"Nguyen","year":"2019","journal-title":"MethodsX"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1016\/j.scitotenv.2019.01.407","article-title":"Global Mapping of Eco-Environmental Vulnerability from Human and Nature Disturbances","volume":"664","author":"Nguyen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2018.11.014","article-title":"Joint Deep Learning for Land Cover and Land Use Classification","volume":"221","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compenvurbsys.2012.06.003","article-title":"A Review of Regional Science Applications of Satellite Remote Sensing in Urban Settings","volume":"37","author":"Patino","year":"2013","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1080\/1747423X.2010.500688","article-title":"Social and Ecological Factors and Land-Use Land-Cover Diversity in Two Provinces in Southeast Asia","volume":"5","author":"Cassidy","year":"2010","journal-title":"J. Land Use Sci."},{"key":"ref_8","first-page":"774","article-title":"Current Status and Future Prospects of Remote Sensing","volume":"32","author":"Bing","year":"2017","journal-title":"Bull. Chin. Acad. Sci. Chin. Version"},{"key":"ref_9","first-page":"633","article-title":"The Cluster Analysis Approaches Based on Geometric Probability and Its Application in the Classification of Remotely Sensed Images","volume":"12","year":"2007","journal-title":"J. Image Graph."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1080\/10106049.2013.768300","article-title":"A Comparative Assessment between Object and Pixel-Based Classification Approaches for Land Use\/Land Cover Mapping Using SPOT 5 Imagery","volume":"29","author":"Tehrany","year":"2014","journal-title":"Geocarto Int."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5770","DOI":"10.1016\/j.asoc.2011.02.030","article-title":"Supervised and Unsupervised Landuse Map Generation from Remotely Sensed Images Using Ant Based Systems","volume":"11","author":"Halder","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1080\/01431161.2014.903435","article-title":"Land-Use\/Cover Classification in a Heterogeneous Coastal Landscape Using RapidEye Imagery: Evaluating the Performance of Random Forest and Support Vector Machines Classifiers","volume":"35","author":"Adam","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","first-page":"245","article-title":"Object Oriented Land Use Classification of Dongjiang River Basin Based on GF-1 Image","volume":"34","author":"Hengkai","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Talukdar, S., Singha, P., Mahato, S., Shahfahad, P.S., Liou, Y.-A., and Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations\u2014A Review. Remote Sens., 12.","DOI":"10.3390\/rs12071135"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15481603.2019.1650447","article-title":"Land Cover and Land Use Classification Performance of Machine Learning Algorithms in a Boreal Landscape Using Sentinel-2 Data","volume":"57","author":"Abdi","year":"2020","journal-title":"GIScience Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107339","DOI":"10.1016\/j.ecolind.2021.107339","article-title":"Evaluating the Suitability of Urban Development Land with a Geodetector","volume":"123","author":"Wang","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_18","unstructured":"Shapiro, L.G., and Stockman, G.C. (2001). Computer Vision, Prentice Hall."},{"key":"ref_19","first-page":"18","article-title":"Research on Progress of Image Semantic Segmentation Based on Deep Learning","volume":"56","author":"Liang","year":"2020","journal-title":"Comput. Eng. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized Cuts and Image Segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1145\/1015706.1015720","article-title":"\u201cGrabCut\u201d: Interactive Foreground Extraction Using Iterated Graph Cuts","volume":"23","author":"Rother","year":"2004","journal-title":"ACM Trans. Graph."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1561\/2000000039","article-title":"Deep Learning: Methods and Applications","volume":"7","author":"Deng","year":"2014","journal-title":"FNT Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017). Densely Connected Convolutional Networks, IEEE.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019). Dual Attention Network for Scene Segmentation. arXiv, 3146\u20133154.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.06.005","article-title":"Developing a Multi-Filter Convolutional Neural Network for Semantic Segmentation Using High-Resolution Aerial Imagery and LiDAR Data","volume":"143","author":"Sun","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","first-page":"715","article-title":"Land use classification of remote sensing images based on multi-scale learning and deep convolution neural network","volume":"47","author":"Wang","year":"2020","journal-title":"J. ZheJiang Univ. Sci. Ed."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban Land-Use Mapping Using a Deep Convolutional Neural Network with High Spatial Resolution Multispectral Remote Sensing Imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015, Springer International Publishing.","DOI":"10.1007\/978-3-319-24553-9"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"CBAM: Convolutional Block Attention Module","volume":"Volume 11211","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014ECCV 2018"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.1007\/s11263-021-01436-0","article-title":"Enhanced 3D Human Pose Estimation from Videos by Using Attention-Based Neural Network with Dilated Convolutions","volume":"129","author":"Liu","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_34","unstructured":"Lafferty, J.D., McCallum, A., and Pereira, F.C.N. (2001). Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Proceedings of the Eighteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc."},{"key":"ref_35","unstructured":"Kr\u00e4henb\u00fchl, P., and Koltun, V. (2011). Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_36","first-page":"254","article-title":"Semantic segmentation of remote sensing image based on deep fusion networks and conditional random field","volume":"24","author":"Xiao","year":"2021","journal-title":"Zggx"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111322","DOI":"10.1016\/j.rse.2019.111322","article-title":"Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models","volume":"237","author":"Tong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018). BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation. arXiv, 325\u2013341.","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hou, B., Liu, Y., Rong, T., Ren, B., Xiang, Z., Zhang, X., and Wang, S. (September, January 2). Panchromatic Image Land Cover Classification Via DCNN with Updating Iteration Strategy. Proceedings of the IGARSS 2020\u20132020 IEEE International Geoscience and Remote Sensing Symposium.","DOI":"10.1109\/IGARSS39084.2020.9323700"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1080\/01431161.2022.2030071","article-title":"A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images","volume":"43","author":"Li","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, C., Li, S., Xiong, D., Fang, P., and Liao, M. (2020). Remote Sensing Image Semantic Segmentation Based on Edge Information Guidance. Remote Sens., 12.","DOI":"10.3390\/rs12091501"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"34858","DOI":"10.1109\/ACCESS.2022.3163535","article-title":"RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-Based Semantic Segmentation","volume":"10","author":"He","year":"2022","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, J., Xiu, J., Yang, Z., and Liu, C. (2020). Dual Path Attention Net for Remote Sensing Semantic Image Segmentation. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9100571"},{"key":"ref_44","unstructured":"Yang, K., Liu, Z., Lu, Q., and Xia, G.-S. (2019). Multi-Scale Weighted Branch Network for Remote Sensing Image Classification, IEEE."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2688\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:24:18Z","timestamp":1760138658000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2688"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,3]]},"references-count":44,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112688"],"URL":"https:\/\/doi.org\/10.3390\/rs14112688","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,3]]}}}