{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T19:29:37Z","timestamp":1772306977304,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,16]],"date-time":"2019-08-16T00:00:00Z","timestamp":1565913600000},"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":["41201468,41701536, 61701047, 41674040"],"award-info":[{"award-number":["41201468,41701536, 61701047, 41674040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of\u00a0Hunan Province","doi-asserted-by":"publisher","award":["2017JJ3322, 2019JJ50639"],"award-info":[{"award-number":["2017JJ3322, 2019JJ50639"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014472","name":"Scientific Research Foundation of Hunan Provincial Education Department","doi-asserted-by":"publisher","award":["16B004,16C0043"],"award-info":[{"award-number":["16B004,16C0043"]}],"id":[{"id":"10.13039\/100014472","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The water and shadow areas in SAR images contain rich information for various applications, which cannot be extracted automatically and precisely at present. To handle this problem, a new framework called Multi-Resolution Dense Encoder and Decoder (MRDED) network is proposed, which integrates Convolutional Neural Network (CNN), Residual Network (ResNet), Dense Convolutional Network (DenseNet), Global Convolutional Network (GCN), and Convolutional Long Short-Term Memory (ConvLSTM). MRDED contains three parts: the Gray Level Gradient Co-occurrence Matrix (GLGCM), the Encoder network, and the Decoder network. GLGCM is used to extract low-level features, which are further processed by the Encoder. The Encoder network employs ResNet to extract features at different resolutions. There are two components of the Decoder network, namely, the Multi-level Features Extraction and Fusion (MFEF) and Score maps Fusion (SF). We implement two versions of MFEF, named MFEF1 and MFEF2, which generate separate score maps. The difference between them lies in that the Chained Residual Pooling (CRP) module is utilized in MFEF2, while ConvLSTM is adopted in MFEF1 to form the Improved Chained Residual Pooling (ICRP) module as the replacement. The two separate score maps generated by MFEF1 and MFEF2 are fused with different weights to produce the fused score map, which is further handled by the Softmax function to generate the final extraction results for water and shadow areas. To evaluate the proposed framework, MRDED is trained and tested with large SAR images. To further assess the classification performance, a total of eight different classification frameworks are compared with our proposed framework. MRDED outperformed by reaching 80.12% in Pixel Accuracy (PA) and 73.88% in Intersection of Union (IoU) for water, 88% in PA and 77.11% in IoU for shadow, and 95.16% in PA and 90.49% in IoU for background classification, respectively.<\/jats:p>","DOI":"10.3390\/s19163576","type":"journal-article","created":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T06:10:14Z","timestamp":1566195014000},"page":"3576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network"],"prefix":"10.3390","volume":"19","author":[{"given":"Peng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science &amp; Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science &amp; Technology, Changsha 410014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lifu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science &amp; Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science &amp; Technology, Changsha 410014, China"},{"name":"School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8054-7449","authenticated-orcid":false,"given":"Zhenhong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"},{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5693-3414","authenticated-orcid":false,"given":"Jin","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuemin","family":"Xing","sequence":"additional","affiliation":[{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science &amp; Technology, Changsha 410014, China"},{"name":"School of Traffic &amp; Transportation Engineering, Changsha University of Science &amp; Technology, Changsha 410114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihui","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science &amp; Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science &amp; Technology, Changsha 410014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.1109\/TGRS.2014.2351417","article-title":"Comparing near-coincident C-and X-band SAR acquisitions of marine oil spills","volume":"53","author":"Skrunes","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1109\/TGRS.2014.2358501","article-title":"Flooding water depth estimation with high-resolution SAR","volume":"53","author":"Iervolino","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Irwin, K., Beaulne, D., Braun, A., and Fotopoulos, G. (2017). Fusion of SAR, optical images and airborne LiDAR for surface water detection. Remote Sens., 9.","DOI":"10.3390\/rs9090890"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Feng, D., Wang, T., and Chen, W. (2015, January 1\u20134). Shadow extraction of buildings from high-resolution SAR data via the combination of amplitude reversal and total variation. Proceedings of the 2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Singapore.","DOI":"10.1109\/APSAR.2015.7306322"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1109\/LGRS.2018.2889299","article-title":"Road Network Extraction from Low-Contrast SAR Images","volume":"16","author":"Zeng","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kottayil, N.K., Zimmer, A., Mukherjee, S., Sun, X., Ghuman, P., and Cheng, I. (2018, January 28\u201331). Accurate Pixel-Based Noise Estimation for InSAR Interferograms. Proceedings of the 2018 IEEE SENSORS, New Delhi, India.","DOI":"10.1109\/ICSENS.2018.8589665"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.isprsjprs.2016.02.009","article-title":"Building detection and building parameter retrieval in InSAR phase images","volume":"114","author":"Dubois","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","first-page":"1634","article-title":"Generation of high precision DEM from TerraSAR-X\/TanDEM-X","volume":"58","author":"Du","year":"2015","journal-title":"Chin. J. Geophys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3220","DOI":"10.1109\/TGRS.2009.2019125","article-title":"Integration of InSAR time-series analysis and water-vapor correction for mapping postseismic motion after the 2003 Bam (Iran) earthquake","volume":"47","author":"Li","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","article-title":"Deep learning-based feature selection for remote sensing scene classification","volume":"12","author":"Zou","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1049\/iet-rsn.2011.0341","article-title":"Fast threshold selection algorithm for segmentation of synthetic aperture radar images","volume":"6","author":"Thiruvengadam","year":"2012","journal-title":"IET Radar Sonar Navig."},{"key":"ref_15","first-page":"1069","article-title":"A novel SAR image segmentation method based on Markov random filed","volume":"29","author":"Hou","year":"2007","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6652","DOI":"10.3390\/s150306652","article-title":"Water Area Extraction Using RADARSAT SAR Images Combined with Landsat Images and Terrain Information","volume":"15","author":"Hong","year":"2015","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3519","DOI":"10.1080\/01431161.2015.1060647","article-title":"Comparing four operational SAR-based water and flood detection approaches","volume":"36","author":"Martinis","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","first-page":"10","article-title":"Water-Body types identification in urban areas from radarsat-2 fully polarimetric SAR data","volume":"50","author":"Xie","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","unstructured":"Cellier, F., Oriot, H., and Nicolas, J.M. (2005, January 10\u201311). Introduction of the mean shift algorithm in SAR images: Application to shadow extraction for building reconstruction. Proceedings of the 25th EARSeL Symposium, Workshop on 3D Remote Sensing, Porto, Portugal."},{"key":"ref_20","unstructured":"Tison, C., Tupin, F., and Maitre, H. (2004, January 20\u201324). Retrieval of building shapes from shadows in high resolution SAR interferometric images. Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jahangir, M., Blacknell, D., Moate, C.P., and Hill, R.D. (2007, January 28\u201329). Extracting information from shadows in SAR images. Proceedings of the 2007 International Conference on Machine Vision, Islamabad, Pakistan.","DOI":"10.1109\/ICMV.2007.4469282"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Papson, S., and Narayanan, R. (2006, January 11\u201313). Modeling of target shadows for SAR image classification. Proceedings of the 35th IEEE Applied Images and Pattern Recognition Workshop, Washington, DC, USA.","DOI":"10.1109\/AIPR.2006.27"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 8\u201310). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_24","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2015, January 7\u20139). Semantic image segmentation with deep convolutional nets and fully connected CRFs. Proceedings of the International Conference on Learning Representations 2015, San Diego, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., and Torr, P.H. (2015, January 8\u201310). Conditional random fields as recurrent neural networks. Proceedings of the IEEE international conference on computer vision, Boston, MA, USA.","DOI":"10.1109\/ICCV.2015.179"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (2017, January 22\u201325). Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, X., Yu, G., Luo, G., and Sun, J. (2017, January 22\u201325). Large Kernel Matters--Improve Semantic Segmentation by Global Convolutional Network. Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.189"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/TGRS.2016.2551720","article-title":"Target classification using the deep convolutional networks for SAR images","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, Z., Pan, Z., and Lei, B. (2017). Transfer learning with deep convolutional neural network for SAR target classification with limited labeled data. Remote Sens., 9.","DOI":"10.3390\/rs9090907"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1109\/LGRS.2017.2698213","article-title":"Deep convolutional highway unit network for sar target classification with limited labeled training data","volume":"14","author":"Lin","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2442","DOI":"10.1109\/TGRS.2016.2645226","article-title":"Deep supervised and contractive neural network for SAR image classification","volume":"55","author":"Geng","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","first-page":"2479","article-title":"A water segmentation method from SAR image based on dense Depthwise separable convolution","volume":"19","author":"Zhang","year":"2019","journal-title":"J. Radars"},{"key":"ref_34","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in neural information processing systems, Lake Tahoe, NV, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 22\u201325). Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_36","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, Z., Gan, Y., Liang, X., Yu, Y., Cheng, H., and Lin, L. (2016, January 11\u201314). Lstm-cf: Unifying context modeling and fusion with lstms for rgb-d scene labeling. Proceedings of the European conference on computer vision, Cham\/Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_34"},{"key":"ref_38","first-page":"22","article-title":"Gray Level Gradient Cooccurrence Matrix Texture Analysis Method","volume":"10","author":"Hong","year":"1984","journal-title":"ACTA Autom. Sin."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/LGRS.2015.2493242","article-title":"SAR image classification via hierarchical sparse representation and multi-size patch features","volume":"13","author":"Hou","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, H., and Dong, F. (2009, January 16\u201319). Image features extraction of gas\/liquid two-phase flow in horizontal pipeline by GLCM and GLGCM. Proceedings of the 2009 9th International Conference on Electronic Measurement & Instruments, Beijing, China.","DOI":"10.1109\/ICEMI.2009.5274632"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/LGRS.2015.2478256","article-title":"High-resolution SAR image classification via deep convolutional autoencoders","volume":"12","author":"Geng","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, L., Cui, X., Li, Z., Yuan, Z., Xing, J., Xing, X., and Jia, Z. (2019). A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration. Sensors, 19.","DOI":"10.3390\/s19112479"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., and Garcia-Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_44","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT press."},{"key":"ref_45","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 11\u201314). Identity mappings in deep residual networks. Proceedings of the European conference on computer vision, Cham\/Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_48","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bilinski, P., and Prisacariu, V. (2018, January 18\u201322). Dense decoder shortcut connections for single-pass semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salty Lake, UT, USA.","DOI":"10.1109\/CVPR.2018.00690"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Mikolov, T., Karafi\u00e1t, M., Burget, L., \u010cernock\u00fd, J., and Khudanpur, S. (2010, January 26\u201330). Recurrent neural network based language model. Proceedings of the Eleventh Annual Conference of the International Speech Communication Association, Chiba, Japan.","DOI":"10.21437\/Interspeech.2010-343"},{"key":"ref_52","unstructured":"Visin, F., Kastner, K., Cho, K., Matteucci, M., Courville, A., and Bengio, Y. (2015). Renet: A recurrent neural network based alternative to convolutional networks. arXiv."},{"key":"ref_53","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, Montreal, QC, Canada."},{"key":"ref_54","unstructured":"Xing, J., and Sieber, R. (2014, January 13\u201318). Sampling based image splitting in large scale distributed computing of earth observation data. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Qu\u00e9bec, QC, Canada."},{"key":"ref_55","first-page":"3","article-title":"Supervised machine learning: A review of classification techniques","volume":"160","author":"Kotsiantis","year":"2007","journal-title":"Emerg. Artif. Intell. Appl. Comput. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Rakhlin, A., Davydow, A., and Nikolenko, S.I. (2018, January 18\u201322). Land Cover Classification from Satellite Imagery with U-Net and Lovasz-Softmax Loss. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00048"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Cao, R., Zhu, J., Tu, W., Li, Q., Cao, J., Liu, B., and Qiu, G. (2018). Integrating Aerial and Street View Images for Urban Land Use Classification. Remote Sens., 10.","DOI":"10.3390\/rs10101553"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yommy, A.S., Liu, R., and Wu, S. (2015, January 26\u201327). SAR image despeckling using refined Lee filter. Proceedings of the 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China.","DOI":"10.1109\/IHMSC.2015.236"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/16\/3576\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:11:41Z","timestamp":1760188301000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/16\/3576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,16]]},"references-count":58,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["s19163576"],"URL":"https:\/\/doi.org\/10.3390\/s19163576","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,16]]}}}