{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T03:54:01Z","timestamp":1771991641875,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFB0504000"],"award-info":[{"award-number":["2017YFB0504000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification.<\/jats:p>","DOI":"10.3390\/rs12223845","type":"journal-article","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T08:18:23Z","timestamp":1606119503000},"page":"3845","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Zhiyu","family":"Xu","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shixin","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Litao","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Li","sequence":"additional","affiliation":[{"name":"The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shicheng","family":"Wang","sequence":"additional","affiliation":[{"name":"The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.ufug.2004.09.001","article-title":"The urban forest in Beijing and its role in air pollution reduction","volume":"3","author":"Yang","year":"2005","journal-title":"Urban For. Urban Green."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.apgeog.2008.08.008","article-title":"Ecological benefits of urban forestry: The case of Kerwa Forest Area (KFA), Bhopal, India","volume":"29","author":"Dwivedi","year":"2009","journal-title":"Appl. Geogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.landurbplan.2011.12.015","article-title":"More green space is linked to less stress in deprived communities: Evidence from salivary cortisol patterns","volume":"105","author":"Thompson","year":"2012","journal-title":"Landsc. Urban Plan."},{"key":"ref_4","first-page":"71","article-title":"Application of 3S technologies in urban green space ecology","volume":"23","author":"Xiao","year":"2004","journal-title":"Chin. J. Ecol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Groenewegen, P.P., Van den Berg, A.E., De Vries, S., and Verheij, R.A. (2006). Vitamin G: Effects of green space on health, well-being, and social safety. BMC Public Health, 6.","DOI":"10.1186\/1471-2458-6-149"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1080\/01431160110075532","article-title":"Monitoring land-use change in the Pearl River Delta using Landsat TM","volume":"23","author":"Seto","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.rse.2005.08.006","article-title":"Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing","volume":"98","author":"Yuan","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.apgeog.2012.06.015","article-title":"Forest cover and deforestation patterns in the Northern Andes (Lake Maracaibo Basin): A synoptic assessment using MODIS and Landsat imagery","volume":"35","author":"Sanchez","year":"2012","journal-title":"Appl. Geogr."},{"key":"ref_9","unstructured":"Hurd, J.D., Wilson, E.H., Lammey, S.G., and Civco, D.L. (2001). Characterization of forest fragmentation and urban sprawl using time sequential Landsat imagery. Proceedings of the ASPRS Annual Convention, Citeseer."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.apgeog.2011.11.010","article-title":"The impacts of Atlanta\u2019s urban sprawl on forest cover and fragmentation","volume":"34","author":"Miller","year":"2012","journal-title":"Appl. Geogr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1080\/01431160500168686","article-title":"An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data","volume":"26","author":"Tucker","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_14","first-page":"248","article-title":"Automatic urban vegetation extraction method using high resolution imagery","volume":"18","author":"Yao","year":"2016","journal-title":"J. Geo Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sirirwardane, M., Gunatilake, J., and Sivanandarajah, S. (2016). Study of the Urban Green Space Planning Using Geographic Information Systems and Remote Sensing Approaches for the City of Colombo, Sri Lanka. Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment, Springer.","DOI":"10.1007\/978-3-319-18663-4_123"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kranj\u010di\u0107, N., Medak, D., \u017dupan, R., and Rezo, M. (2019). Machine learning methods for classification of the green infrastructure in city areas. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8100463"},{"key":"ref_17","first-page":"291","article-title":"Study of Urban Green Space Surveying Based on High Resolution Images of Remote Sensing","volume":"26","author":"Jianhui","year":"2010","journal-title":"Resour. Dev. Mark."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1007\/s10980-015-0195-3","article-title":"Quantifying spatiotemporal pattern of urban greenspace: New insights from high resolution data","volume":"30","author":"Qian","year":"2015","journal-title":"Landsc. Ecol."},{"key":"ref_19","first-page":"68","article-title":"Detecting urban vegetation efficiently with high resolution remote sensing data","volume":"8","author":"Huang","year":"2004","journal-title":"J. Remote Sens. Beijing"},{"key":"ref_20","first-page":"3221","article-title":"Urban building green environment index based on LiDAR and multispectral data","volume":"38","author":"Meng","year":"2019","journal-title":"Chin. J. Ecol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2612","DOI":"10.1109\/JSTARS.2019.2906387","article-title":"Densely based multi-scale and multi-modal fully convolutional networks for high-resolution remote-sensing image semantic segmentation","volume":"12","author":"Peng","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","unstructured":"Xu, Z., Zhou, Y., Wang, S., Wang, L., and Wang, Z. (2021). U-Net for urban green space classification in GF-2 remote sensing images. J. Image Graph., in press."},{"key":"ref_23","first-page":"702","article-title":"Aircraft classification in remote-sensing images using convolutional neural networks","volume":"22","author":"Zhou","year":"2017","journal-title":"J. Image Graph."},{"key":"ref_24","first-page":"385","article-title":"Interchange Recognition Method Based on CNN","volume":"47","author":"Haiwei","year":"2018","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hamaguchi, R., and Hikosaka, S. (2018, January 18\u201322). Building detection from satellite imagery using ensemble of size-specific detectors. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00041"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhao, K., Kang, J., Jung, J., and Sohn, G. (2018, January 18\u201322). Building Extraction From Satellite Images Using Mask R-CNN With Building Boundary Regularization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00045"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, X., Sun, H., Fu, K., Yang, J., Sun, X., Yan, M., and Guo, Z. (2018). Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks. Remote Sens., 10.","DOI":"10.3390\/rs10010132"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Ehrlich, M., Shah, S., Davis, L.S., and Chellappa, R. (2018, January 18\u201322). Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00047"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pascual, G., Segu\u00ed, S., and Vitri\u00e0, J. (2018, January 19\u201321). Uncertainty Gated Network for Land Cover Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00052"},{"key":"ref_30","first-page":"999","article-title":"Change Detection Method for High Resolution Remote Sensing Images Using Deep Learning","volume":"46","author":"Zhang","year":"2017","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D convolutional neural networks for human action recognition","volume":"35","author":"Ji","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Panboonyuen, T., Jitkajornwanich, K., Lawawirojwong, S., Srestasathiern, P., and Vateekul, P. (2017). Road segmentation of remotely-sensed images using deep convolutional neural networks with landscape metrics and conditional random fields. Remote Sens., 9.","DOI":"10.20944\/preprints201706.0012.v3"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wu, G., Shao, X., Guo, Z., Chen, Q., Yuan, W., Shi, X., Xu, Y., and Shibasaki, R. (2018). Automatic building segmentation of aerial imagery using multi-constraint fully convolutional networks. Remote Sens., 10.","DOI":"10.3390\/rs10030407"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_36","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 8\u201310). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_39","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 (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 16\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_43","first-page":"420","article-title":"High resolution remote sensing image classification based on multi-scale and multi-feature fusion","volume":"33","author":"Hui","year":"2016","journal-title":"Chin. J. Quantum Electron."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/LGRS.2018.2795531","article-title":"Fully convolutional networks for semantic segmentation of very high resolution remotely sensed images combined with DSM","volume":"15","author":"Sun","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.rse.2014.10.018","article-title":"Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery","volume":"156","author":"Senf","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ulsig, L., Nichol, C.J., Huemmrich, K.F., Landis, D.R., Middleton, E.M., Lyapustin, A.I., Mammarella, I., Levula, J., and Porcar-Castell, A. (2017). Detecting inter-annual variations in the phenology of evergreen conifers using long-term MODIS vegetation index time series. Remote Sens., 9.","DOI":"10.3390\/rs9010049"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.ufug.2018.01.021","article-title":"Mapping vegetation functional types in urban areas with WorldView-2 imagery: Integrating object-based classification with phenology","volume":"31","author":"Yan","year":"2018","journal-title":"Urban For. Urban Green."},{"key":"ref_48","unstructured":"Beijing Gardening and Greening Bureau (2020, November 03). Work Summary in 2019 and Work Plan in 2020 of Beijing Gardening and Greening Bureau, Available online: http:\/\/yllhj.beijing.gov.cn\/zwgk\/ghxx\/jhzj\/202002\/t20200227_1670249.shtml."},{"key":"ref_49","unstructured":"Beijing Gardening and Greening Bureau (2020, November 03). Notice on Printing and Distributing the Key Points of Urban Greening Work in 2020, Available online: http:\/\/yllhj.beijing.gov.cn\/zwgk\/fgwj\/qtwj\/202001\/t20200121_1619893.shtml."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"104458","DOI":"10.1016\/j.catena.2020.104458","article-title":"Effectiveness assessment of keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area","volume":"188","author":"Nhu","year":"2020","journal-title":"Catena"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhou, Y., Wang, S., Wang, F., and Xu, Z. (2021). House building extraction from high resolution remote sensing image based on IEU-Net. J. Remote Sens., in press.","DOI":"10.11834\/jrs.20210042"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Salehi, S.S.M., Erdogmus, D., and Gholipour, A. (2017). Tversky loss function for image segmentation using 3D fully convolutional deep networks. International Workshop on Machine Learning in Medical Imaging, Springer.","DOI":"10.1007\/978-3-319-67389-9_44"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Peng, Y., Zhang, Z., He, G., and Wei, M. (2019). An improved grabcut method based on a visual attention model for rare-earth ore mining area recognition with high-resolution remote sensing images. Remote Sens., 11.","DOI":"10.3390\/rs11080987"},{"key":"ref_55","unstructured":"Zhang, Z., and Sabuncu, M. (2018, January 3\u20138). Generalized cross entropy loss for training deep neural networks with noisy labels. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Montr\u00e9al, QC, Canada."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3845\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:36:08Z","timestamp":1760178968000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3845"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,23]]},"references-count":56,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12223845"],"URL":"https:\/\/doi.org\/10.3390\/rs12223845","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,23]]}}}