{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T07:56:24Z","timestamp":1777103784407,"version":"3.51.4"},"reference-count":89,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"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":["61731022"],"award-info":[{"award-number":["61731022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61531019"],"award-info":[{"award-number":["61531019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19010401"],"award-info":[{"award-number":["XDA19010401"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs.<\/jats:p>","DOI":"10.3390\/rs13214237","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T23:27:39Z","timestamp":1634858859000},"page":"4237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["High-Resolution Boundary Refined Convolutional Neural Network for Automatic Agricultural Greenhouses Extraction from GaoFen-2 Satellite Imageries"],"prefix":"10.3390","volume":"13","author":[{"given":"Xiaoping","family":"Zhang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Bo","family":"Cheng","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Jinfen","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1499-7177","authenticated-orcid":false,"given":"Chenbin","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"ref_1","unstructured":"Cantliffe, D.J. (2001, January 13\u201314). Protected agriculture\u2014A regional solution for water scarcity and production of high-value crops in the Jordan Valley. Proceedings of the Water in the Jordan Valley: Technical Solutions and Regional Cooperation Conference, Norman, OK, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1080\/01431160600658156","article-title":"Remote sensing as a tool for monitoring plasticulture in agricultural landscapes","volume":"28","author":"Levin","year":"2007","journal-title":"Int. J. Remote. Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.landurbplan.2010.11.008","article-title":"Analysis of plasticulture landscapes in Southern Italy through remote sensing and solid modelling techniques","volume":"100","author":"Picuno","year":"2011","journal-title":"Landsc. Urban Plan."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1080\/03602559.2014.886056","article-title":"Innovative material and improved technical design for a sustainable exploitation of agricultural plastic film","volume":"53","author":"Picuno","year":"2014","journal-title":"Polym.-Plast. Technol. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.biosystemseng.2018.08.009","article-title":"Methodological proposal to assess plastic greenhouses land cover change from the combination of archival aerial orthoimages and Landsat data","volume":"175","author":"Aguilar","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_6","first-page":"30","article-title":"\u201cPlasticulture\u201d magazine: A milestone for a history of progress in plasticulture","volume":"1","author":"Garnaud","year":"2000","journal-title":"Plasticulture"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.biosystemseng.2016.10.018","article-title":"Analysis of the collapse of a greenhouse with vaulted roof","volume":"151","author":"Briassoulis","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3554","DOI":"10.3390\/rs6053554","article-title":"Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery","volume":"6","author":"Aguilar","year":"2014","journal-title":"Remote. Sens."},{"key":"ref_9","first-page":"79","article-title":"Object-based classification approach for greenhouse mapping using Landsat-8 imagery","volume":"9","author":"Chaofan","year":"2016","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_10","unstructured":"National Bureau of Statistics (2017, December 15). Communiqu\u00e9 on Major Data of the Third National Agricultural Census (No. 2), Available online: http:\/\/www.stats.gov.cn\/tjsj\/tjgb\/nypcgb\/qgnypcgb\/201712\/t20171215_1563539.html."},{"key":"ref_11","unstructured":"Sica, C., and Picuno, P. (2007, January 4\u20136). Spectro-radiometrical characterization of plastic nets for protected cultivation. Proceedings of the International Symposium on High Technology for Greenhouse System Management: Greensys, Naples, Italy."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.1016\/j.polymdegradstab.2012.06.024","article-title":"Experimental tests and technical characteristics of regenerated films from agricultural plastics","volume":"97","author":"Picuno","year":"2012","journal-title":"Polym. Degrad. Stab."},{"key":"ref_13","unstructured":"Knickel, K. (2021, October 21). Changes in Farming Systems, Landscape, and Nature: Key Success Factors of Agri-Environmental Schemes (AES); na: 2000. Available online: https:\/\/eurekamag.com\/research\/003\/378\/003378043.php."},{"key":"ref_14","first-page":"78","article-title":"Study on changes of soil salt and nutrient in greenhouse of different planting years","volume":"2","author":"Du","year":"2007","journal-title":"J. Soil Water Conserv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.isprsjprs.2017.03.002","article-title":"Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index","volume":"128","author":"Yang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3146","DOI":"10.1080\/01431161.2020.1871100","article-title":"A semantic segmentation method with category boundary for Land Use and Land Cover (LULC) mapping of Very-High Resolution (VHR) remote sensing image","volume":"42","author":"Xu","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.isprsjprs.2018.02.013","article-title":"Remote sensing monitoring of the impact of a major mining wastewater disaster on the turbidity of the Doce River plume off the eastern Brazilian coast","volume":"145","author":"Rudorff","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lynch, P., Blesius, L., and Hines, E. (2020). Classification of Urban Area Using Multispectral Indices for Urban Planning. Remote Sens., 12.","DOI":"10.3390\/rs12152503"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103921","DOI":"10.1016\/j.landurbplan.2020.103921","article-title":"Remote sensing in urban planning: Contributions towards ecologically sound policies?","volume":"204","author":"Wellmann","year":"2020","journal-title":"Landsc. Urban Plan."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, M., Zhang, Z., Lei, L., Wang, X., and Guo, X. (2020). Agricultural Greenhouses Detection in High-Resolution Satellite Images Based on Convolutional Neural Networks: Comparison of Faster R-CNN, YOLO v3 and SSD. Sensors, 20.","DOI":"10.3390\/s20174938"},{"key":"ref_21","first-page":"30","article-title":"Next generation of global land cover characterization, mapping, and monitoring","volume":"25","author":"Giri","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"073553","DOI":"10.1117\/1.JRS.7.073553","article-title":"Evaluation of different classification techniques for the detection of glass and plastic greenhouses from WorldView-2 satellite imagery","volume":"7","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"097094","DOI":"10.1117\/1.JRS.9.097094","article-title":"Threshold model for detecting transparent plastic-mulched landcover using moderate-resolution imaging spectroradiometer time series data: A case study in southern Xinjiang, China","volume":"9","author":"Lu","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, Z., Li, F. (2017). Mapping Plastic-Mulched Farmland with C-Band Full Polarization SAR Remote Sensing Data. Remote Sens., 9.","DOI":"10.3390\/rs9121264"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wu, L., Xie, Z., and Chen, Z. (2018). Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters. Remote Sens., 10.","DOI":"10.3390\/rs10010144"},{"key":"ref_26","first-page":"403","article-title":"Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almer\u00eda (Spain)","volume":"52","author":"Novelli","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Balcik, F.B., Senel, G., and Goksel, C. (2019, January 16\u201319). Greenhouse Mapping using Object Based Classification and Sentinel-2 Satellite Imagery. Proceedings of the 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Istanbul, Turkey.","DOI":"10.1109\/Agro-Geoinformatics.2019.8820252"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez-Lao, R., Aguilar, F.J., Nemmaoui, A., and Aguilar, M.A. (2020). Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland: An Analysis of Worldwide Research. Remote Sens., 12.","DOI":"10.3390\/rs12162649"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.04.031","article-title":"A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems","volume":"196","author":"Wang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_32","first-page":"1","article-title":"Applications of deep convolutional neural network in computer vision","volume":"31","author":"Hongtao","year":"2016","journal-title":"J. Data Acquis. Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\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_34","doi-asserted-by":"crossref","unstructured":"Eigen, D., and Fergus, R. (2015, January 7\u201313). 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_35","doi-asserted-by":"crossref","unstructured":"Mostajabi, M., Yadollahpour, P., and Shakhnarovich, G. (2015, January 7\u201312). Feedforward semantic segmentation with zoom-out features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298959"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, L. (2019). Deep Residual Autoencoder with Multiscaling for Semantic Segmentation of Land-Use Images. Remote Sens., 11.","DOI":"10.3390\/rs11182142"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, J., Ding, C.H.Q., Chen, S., He, C., and Luo, B. (2020). Semi-Supervised Remote Sensing Image Semantic Segmentation via Consistency Regularization and Average Update of Pseudo-Label. Remote Sens., 12.","DOI":"10.3390\/rs12213603"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, W., Chen, X., Ran, J., Liu, L., Wang, Q., Xin, L., and Li, G. (2021). LaeNet: A Novel Lightweight Multitask CNN for Automatically Extracting Lake Area and Shoreline from Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13010056"},{"key":"ref_39","first-page":"73","article-title":"Understanding Deep Learning Techniques for Image Segmentation","volume":"52","author":"Ghosh","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_40","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_41","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_42","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_43","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_44","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_45","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_46","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 (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_48","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., and Wang, J. (2019). High-resolution representations for labeling pixels and regions. arXiv."},{"key":"ref_49","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-local neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., Wei, F., and Hu, H. (2019, January 27\u201328). Gcnet: Non-local networks meet squeeze-excitation networks and beyond. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 16\u201317). Dual attention network for scene segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017, January 21\u201324). Understanding of a convolutional neural network. Proceedings of the 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1073\/pnas.1715832114","article-title":"A mixed-scale dense convolutional neural network for image analysis","volume":"115","author":"Pelt","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sultana, F., Sufian, A., and Dutta, P. (2018, January 22\u201323). Advancements in Image Classification using Convolutional Neural Network. Proceedings of the 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata, India.","DOI":"10.1109\/ICRCICN.2018.8718718"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Onim, M.S.H., Ehtesham, A.R.B., Anbar, A., Islam, A.N., and Rahman, A.M. (2020, January 28\u201329). LULC classification by semantic segmentation of satellite images using FastFCN. Proceedings of the 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), Dhaka, Bangladesh.","DOI":"10.1109\/ICAICT51780.2020.9333522"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yang, Q., Liu, M., Zhang, Z., Yang, S., Ning, J., and Han, W. (2019). Mapping Plastic Mulched Farmland for High Resolution Images of Unmanned Aerial Vehicle Using Deep Semantic Segmentation. Remote Sens., 11.","DOI":"10.3390\/rs11172008"},{"key":"ref_61","unstructured":"Baghirli, O., Ibrahimli, I., and Mammadzada, T. (2020). Greenhouse Segmentation on High-Resolution Optical Satellite Imagery Using Deep Learning Techniques. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Sun, H., Wang, L., Lin, R., Zhang, Z., and Zhang, B. (2021). Mapping Plastic Greenhouses with Two-Temporal Sentinel-2 Images and 1D-CNN Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13142820"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00c3\u00a1r, 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"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3308","DOI":"10.1080\/01431161.2018.1528024","article-title":"A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery","volume":"40","author":"Ji","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Sun, G., Huang, H., Zhang, A., Li, F., Zhao, H., and Fu, H. (2019). Fusion of Multiscale Convolutional Neural Networks for Building Extraction in Very High-Resolution Images. Remote Sens., 11.","DOI":"10.3390\/rs11030227"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018, January 18\u201323). Learning a discriminative feature network for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00199"},{"key":"ref_67","unstructured":"Takikawa, T., Acuna, D., Jampani, V., and Fidler, S. (November, January 27). Gated-scnn: Gated shape cnns for semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_68","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_69","doi-asserted-by":"crossref","unstructured":"Ou, C., Yang, J., Du, Z., Liu, Y., Feng, Q., and Zhu, D. (2020). Long-term mapping of a greenhouse in a typical protected agricultural region using landsat imagery and the google earth engine. Remote Sens., 12.","DOI":"10.3390\/rs12010055"},{"key":"ref_70","unstructured":"China Centre for Resources Satellite Data and Application (2014, October 15). GaoFen-2. Available online: http:\/\/www.cresda.com\/CN\/Satellite\/3128.shtml."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.isprsjprs.2009.12.004","article-title":"Bias-corrected rational polynomial coefficients for high accuracy geo-positioning of QuickBird stereo imagery","volume":"65","author":"Tong","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TGRS.2014.2361734","article-title":"A Critical Comparison Among Pansharpening Algorithms","volume":"53","author":"Vivone","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Guo, M., Yu, Z., Xu, Y., Huang, Y., and Li, C. (2021). ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data. Remote Sens., 13.","DOI":"10.3390\/rs13071292"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Yin, W., Diao, W., Wang, P., Gao, X., Li, Y., and Sun, X. (2021). PCAN\u2014Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13071243"},{"key":"ref_75","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., and Liu, W. (November, January 27). Ccnet: Criss-cross attention for semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_76","unstructured":"Zhang, H., Zu, K., Lu, J., Zou, Y., and Meng, D. (2021). Epsanet: An efficient pyramid split attention block on convolutional neural network. arXiv."},{"key":"ref_77","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (Icml), Haifa, Israel."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). 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_79","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network in network. arXiv."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Zhen, M., Wang, J., Zhou, L., Li, S., Shen, T., Shang, J., Fang, T., and Quan, L. (2020, January 13\u201319). Joint semantic segmentation and boundary detection using iterative pyramid contexts. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01368"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Xie, S., and Tu, Z. (2015, January 13\u201316). Holistically-nested edge detection. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.164"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Yu, Z., Feng, C., Liu, M.-Y., and Ramalingam, S. (2017, January 21\u201326). Casenet: Deep category-aware semantic edge detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.191"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Acuna, D., Kar, A., and Fidler, S. (2019, January 15\u201320). Devil is in the edges: Learning semantic boundaries from noisy annotations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01133"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Zhang, Z. (2018, January 4\u20136). Improved adam optimizer for deep neural networks. Proceedings of the 2018 IEEE\/ACM 26th International Symposium on Quality of Service (IWQoS), Banff, AB, Canada.","DOI":"10.1109\/IWQoS.2018.8624183"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road Extraction by Deep Residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1109\/TGRS.2020.2994150","article-title":"LANet: Local Attention Embedding to Improve the Semantic Segmentation of Remote Sensing Images","volume":"59","author":"Ding","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"6169","DOI":"10.1109\/TGRS.2020.3026051","article-title":"MAP-Net: Multiple Attending Path Neural Network for Building Footprint Extraction From Remote Sensed Imagery","volume":"59","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Jin, Y., Xu, W., Hu, Z., Jia, H., Luo, X., and Shao, D. (2020). GSCA-UNet: Towards Automatic Shadow Detection in Urban Aerial Imagery with Global-Spatial-Context Attention Module. 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