{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T22:26:34Z","timestamp":1766787994022,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"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":["41401374","41771451"],"award-info":[{"award-number":["41401374","41771451"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFB0501403"],"award-info":[{"award-number":["2016YFB0501403"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The classification of very-high-resolution (VHR) remote sensing images is essential in many applications. However, high intraclass and low interclass variations in these kinds of images pose serious challenges. Fully convolutional network (FCN) models, which benefit from a powerful feature learning ability, have shown impressive performance and great potential. Nevertheless, only classification results with coarse resolution can be obtained from the original FCN method. Deep feature fusion is often employed to improve the resolution of outputs. Existing strategies for such fusion are not capable of properly utilizing the low-level features and considering the importance of features at different scales. This paper proposes a novel, end-to-end, fully convolutional network to integrate a multiconnection ResNet model and a class-specific attention model into a unified framework to overcome these problems. The former fuses multilevel deep features without introducing any redundant information from low-level features. The latter can learn the contributions from different features of each geo-object at each scale. Extensive experiments on two open datasets indicate that the proposed method can achieve class-specific scale-adaptive classification results and it outperforms other state-of-the-art methods. The results were submitted to the International Society for Photogrammetry and Remote Sensing (ISPRS) online contest for comparison with more than 50 other methods. The results indicate that the proposed method (ID: SWJ_2) ranks #1 in terms of overall accuracy, even though no additional digital surface model (DSM) data that were offered by ISPRS were used and no postprocessing was applied.<\/jats:p>","DOI":"10.3390\/rs11131617","type":"journal-article","created":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T11:02:37Z","timestamp":1562583757000},"page":"1617","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Deep Feature Fusion with Integration of Residual Connection and Attention Model for Classification of VHR Remote Sensing Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2744-7059","authenticated-orcid":false,"given":"Jicheng","family":"Wang","sequence":"first","affiliation":[{"name":"State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China"},{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenfan","family":"Qiao","sequence":"additional","affiliation":[{"name":"State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanshuai","family":"Dai","sequence":"additional","affiliation":[{"name":"State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1507-323X","authenticated-orcid":false,"given":"Zhilin","family":"Li","sequence":"additional","affiliation":[{"name":"State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China"},{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5345","DOI":"10.1080\/01431161.2017.1410300","article-title":"What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?","volume":"39","author":"Hunt","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2017.07.007","article-title":"Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak","volume":"131","author":"Dash","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9458","DOI":"10.3390\/rs6109458","article-title":"Remote Sensing Image Interpretation for Urban Environment Analysis: Methods, System and Examples","volume":"6","author":"Du","year":"2014","journal-title":"Remote Sens."},{"key":"ref_4","unstructured":"Sevilla-Lara, L., Sun, D., Jampani, V., and Black, M.J. (July, January 26). Optical flow with semantic segmentation and localized layers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_5","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":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shen, L., Wu, L., Dai, Y., Qiao, W., and Wang, Y. (2017). Topic modelling for object-based unsupervised classification of VHR panchromatic satellite images based on multiscale image segmentation. Remote Sens., 9.","DOI":"10.3390\/rs9080840"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3345","DOI":"10.1109\/TGRS.2016.2516042","article-title":"PW-COG: An effective texture descriptor for VHR satellite imagery using a pointwise approach on covariance matrix of oriented gradients","volume":"54","author":"Pham","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, X., Du, S., Wang, Q., and Zhou, W. (2018). Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images. Remote Sens., 10.","DOI":"10.3390\/rs10020281"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.1109\/JSTARS.2014.2386902","article-title":"Pointwise graph-based local texture characterization for very high resolution multispectral image classification","volume":"8","author":"Pham","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1109\/LGRS.2013.2280298","article-title":"A semisupervised latent dirichlet allocation model for object-based classification of VHR panchromatic satellite images","volume":"11","author":"Shen","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.isprsjprs.2018.01.021","article-title":"Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models","volume":"145","author":"Marcos","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_14","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ye, D., Li, Y., Tao, C., Xie, X., and Wang, X. (2017). Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6110364"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5653","DOI":"10.1109\/TGRS.2017.2711275","article-title":"Integrating Multilayer Features of Convolutional Neural Networks for Remote Sensing Scene Classification","volume":"55","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4799","DOI":"10.1109\/TGRS.2019.2893115","article-title":"A Deep Scene Representation for Aerial Scene Classification","volume":"57","author":"Zheng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2018.06.034","article-title":"An object-based convolutional neural network (OCNN) for urban land use classification","volume":"216","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_19","unstructured":"LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., and Jackel, L.D. (1990, January 26\u201329). Handwritten digit recognition with a back-propagation network. Proceedings of the Advances in Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 8\u201312). 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_21","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, Y., Liu, G., and Jiao, L. (2018). A Novel Deep Fully Convolutional Network for PolSAR Image Classification. Remote Sens., 10.","DOI":"10.3390\/rs10121984"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Fu, G., Liu, C., Zhou, R., Sun, T., and Zhang, Q. (2017). Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sens., 9.","DOI":"10.3390\/rs9050498"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 13\u201316). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2017.12.007","article-title":"Semantic labeling in very high resolution images via a self-cascaded convolutional neural network","volume":"145","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, Y., Zhang, Q., Xiang, S., and Pan, C. (2017). Gated convolutional neural network for semantic segmentation in high-resolution images. Remote Sens., 9.","DOI":"10.3390\/rs9050446"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1782","DOI":"10.1109\/LGRS.2016.2608963","article-title":"Hyperspectral image classification based on nonlinear spectral\u2013spatial network","volume":"13","author":"Pan","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gaetano, R., Ienco, D., Ose, K., and Cresson, R. (2018). A Two-Branch CNN Architecture for Land Cover Classification of PAN and MS Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10111746"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Perez, D., Banerjee, D., Kwan, C., Dao, M., Shen, Y., Koperski, K., Marchisio, G., and Li, J. (2017, January 19\u201321). Deep learning for effective detection of excavated soil related to illegal tunnel activities. Proceedings of the IEEE Ubiquitous Computing, Electronics and Mobile Communication Conference, New York, NY, USA.","DOI":"10.1109\/UEMCON.2017.8249062"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lu, Y., Perez, D., Dao, M., Kwan, C., and Li, J. (2018, January 8\u201310). Deep Learning with Synthetic Hyperspectral Images for Improved Soil Detection in Multispectral Imagery. Proceedings of the IEEE Ubiquitous Computing, Electronics and Mobile Communication Conference, New York, NY, USA.","DOI":"10.1109\/UEMCON.2018.8796838"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3386","DOI":"10.1109\/JSTARS.2017.2680324","article-title":"Object-based convolutional neural network for high-resolution imagery classification","volume":"10","author":"Zhao","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional neural networks for large-scale remote-sensing image classification","volume":"55","author":"Maggiori","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Li, J., Cui, W., and Jiang, H. (2016, January 10\u201315). Fully convolutional networks for building and road extraction: Preliminary results. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729406"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, H., Wu, P., Yao, X., Wu, Y., Wang, B., and Xu, Y. (2018). Building extraction in very high resolution imagery by dense-attention networks. Remote Sens., 10.","DOI":"10.3390\/rs10111768"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Mboga, N., Georganos, S., Grippa, T., Lennert, M., Vanhuysse, S., and Wolff, E. (2019). Fully Convolutional Networks and Geographic Object-Based Image Analysis for the Classification of VHR Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11050597"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2017.11.011","article-title":"Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks","volume":"140","author":"Audebert","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2018.08.022","article-title":"A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter","volume":"217","author":"Cao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.11.009","article-title":"Classification with an edge: Improving semantic image segmentation with boundary detection","volume":"135","author":"Marmanis","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, W., Huang, H., Schmitz, M., Sun, X., Wang, H., and Mayer, H. (2017). Effective fusion of multi-modal remote sensing data in a fully convolutional network for semantic labeling. Remote Sens., 10.","DOI":"10.3390\/rs10010052"},{"key":"ref_42","unstructured":"Liu, W., Rabinovich, A., and Berg, A.C. (2015). Parsenet: Looking wider to see better. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_44","unstructured":"Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning hierarchical features for scene labeling","volume":"35","author":"Farabet","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Eigen, D., and Fergus, R. (2015, January 13\u201316). Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.304"},{"key":"ref_47","unstructured":"Lin, G., Shen, C., Van Den Hengel, A., and Reid, I. (July, January 26). Efficient piecewise training of deep structured models for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_48","unstructured":"Chen, L.-C., Yang, Y., Wang, J., Xu, W., and Yuille, A.L. (July, January 26). Attention to scale: Scale-aware semantic image segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_49","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_50","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT press."},{"key":"ref_51","unstructured":"Mnih, V. (2013). Machine Learning for Aerial Image Labeling, University of Toronto (Canada)."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, X., Yu, G., Luo, G., and Sun, J. (2017, January 21\u201326). Large Kernel Matters\u2014Improve 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_53","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (2017, January 21\u201326). 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_54","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_56","unstructured":"Sherrah, J. (2016). Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/JSTARS.2018.2810320","article-title":"Symmetrical dense-shortcut deep fully convolutional networks for semantic segmentation of very-high-resolution remote sensing images","volume":"11","author":"Chen","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1080\/15481603.2018.1564499","article-title":"Semantic segmentation of high spatial resolution images with deep neural networks","volume":"56","author":"Yang","year":"2019","journal-title":"GISci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Piramanayagam, S., Schwartzkopf, W., Koehler, F.W., and Saber, E. (2016, January 26\u201329). Classification of remote sensed images using random forests and deep learning framework. Proceedings of the SPIE Remote Sensing, Scotland, UK.","DOI":"10.1117\/12.2243169"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"016501","DOI":"10.1117\/1.JRS.13.016501","article-title":"Semantic segmentation of multisensor remote sensing imagery with deep ConvNets and higher-order conditional random fields","volume":"13","author":"Liu","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1617\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:03:33Z","timestamp":1760187813000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1617"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,8]]},"references-count":61,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11131617"],"URL":"https:\/\/doi.org\/10.3390\/rs11131617","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,7,8]]}}}