{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T17:25:12Z","timestamp":1770225912985,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T00:00:00Z","timestamp":1588896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Basic Research Program of China (973 Program)","award":["No.2016YFC0803000"],"award-info":[{"award-number":["No.2016YFC0803000"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41371342"],"award-info":[{"award-number":["No. 41371342"]}],"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":["No. 61331016"],"award-info":[{"award-number":["No. 61331016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Semantic segmentation is an important field for automatic processing of remote sensing image data. Existing algorithms based on Convolution Neural Network (CNN) have made rapid progress, especially the Fully Convolution Network (FCN). However, problems still exist when directly inputting remote sensing images to FCN because the segmentation result of FCN is not fine enough, and it lacks guidance for prior knowledge. To obtain more accurate segmentation results, this paper introduces edge information as prior knowledge into FCN to revise the segmentation results. Specifically, the Edge-FCN network is proposed in this paper, which uses the edge information detected by Holistically Nested Edge Detection (HED) network to correct the FCN segmentation results. The experiment results on ESAR dataset and GID dataset demonstrate the validity of Edge-FCN.<\/jats:p>","DOI":"10.3390\/rs12091501","type":"journal-article","created":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T11:26:00Z","timestamp":1588937160000},"page":"1501","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["Remote Sensing Image Semantic Segmentation Based on Edge Information Guidance"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3662-5769","authenticated-orcid":false,"given":"Chu","family":"He","sequence":"first","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"},{"name":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Shenglin","family":"Li","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Dehui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Peizhang","family":"Fang","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Mingsheng","family":"Liao","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4576","DOI":"10.1109\/TGRS.2012.2236338","article-title":"Texture Classification of PolSAR Data Based on Sparse Coding of Wavelet Polarization Textons","volume":"51","author":"He","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Liu, C., Zeng, D., Wu, H., Wang, Y., Jia, S., and Xin, L. (2020). Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network. Remote Sens., 12.","DOI":"10.3390\/rs12020311"},{"key":"ref_4","first-page":"640","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Long","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","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_6","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_7","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_8","unstructured":"Shuai, Z., Jayasumana, S., Romeraparedes, B., Vineet, V., Su, Z., Du, D., Chang, H., and Torr, P.H.S. (2015, January 7\u201313). Conditional Random Fields as Recurrent Neural Networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, L., Liu, Z., Ren, T., Liu, D., Ma, Z., Tong, L., Zhang, C., Zhou, T., Zhang, X., and Li, S. (2020). Identification of Seed Maize Fields With High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier. Remote Sens., 12.","DOI":"10.3390\/rs12030362"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","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_12","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 & Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pan, X., Gao, L., Marinoni, A., Zhang, B., Yang, F., and Gamba, P. (2018). Semantic Labeling of High Resolution Aerial Imagery and LiDAR Data with Fine Segmentation Network. Remote Sens., 10.","DOI":"10.3390\/rs10050743"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Xia, W., Ma, C., Liu, J., Liu, S., Chen, F., Yang, Z., and Duan, J. (2019). High-Resolution Remote Sensing Imagery Classification of Imbalanced Data Using Multistage Sampling Method and Deep Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11212523"},{"key":"ref_16","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 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2016). Pyramid Scene Parsing Network. arXiv.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_18","unstructured":"Chao, P., Zhang, X., Gang, Y., Luo, G., and Jian, S. (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."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, C., Wang, S., Li, J., Li, F., Yang, X., Wang, Y., and Yin, L. (2019). Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network. Appl. Sci., 9.","DOI":"10.3390\/app9142917"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, S., Zhu, X., and Bao, J. (2019). Hierarchical Multi-Scale Convolutional Neural Networks for Hyperspectral Image Classification. Sensors, 19.","DOI":"10.3390\/s19071714"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, Y., He, C., Liu, X., and Liao, M. (2018). A Hierarchical Fully Convolutional Network Integrated with Sparse and Low-Rank Subspace Representations for PolSAR Imagery Classification. Remote Sens., 10.","DOI":"10.3390\/rs10020342"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, C., Fang, P., Zhang, Z., Xiong, D., and Liao, M. (2019). An End-to-End Conditional Random Fields and Skip-Connected Generative Adversarial Segmentation Network for Remote Sensing Images. Remote Sens., 11.","DOI":"10.3390\/rs11131604"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Adly, H., and Moustafa, M. (2017, January 17\u201319). A Hybrid Deep Learning Approach for Texture Analysis. Proceedings of the 2017 2nd International Conference on Multimedia and Image Processing (ICMIP), Wuhan, China.","DOI":"10.1109\/ICMIP.2017.5"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"3943","DOI":"10.1080\/0143116042000192321","article-title":"A comparison of texture measures for the per-field classification of Mediterranean land cover","volume":"25","author":"Lloyd","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/MITS.2018.2842040","article-title":"Road Traffic Conditions Classification Based on Multilevel Filtering of Image Content Using Convolutional Neural Networks","volume":"10","author":"Pamula","year":"2018","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cheng, M.M., Hu, X., Wang, K., and Bai, X. (2017, January 21\u201326). Richer Convolutional Features for Edge Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.622"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, J., Price, B., Cohen, S., Lee, H., and Yang, M.H. (2016, January 27\u201330). Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.28"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Chao, P., Gao, C., and Nong, S. (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_31","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Barron, J.T., Papandreou, G., Murphy, K., and Yuille, A.L. (2016, January 27\u201330). Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform. Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.492"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, C., Shi, Z., Fang, P., Xiong, D., He, B., and Liao, M. (2020). Edge Prior Multilayer Segmentation Network Based on Bayesian Framework. J. Sens., 2020.","DOI":"10.1155\/2020\/6854260"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"137080","DOI":"10.1109\/ACCESS.2019.2932229","article-title":"Relationship Prior and Adaptive Knowledge Mimic Based Compressed Deep Network for Aerial Scene Classification","volume":"7","author":"He","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"6","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s11263-017-1004-z","article-title":"Holistically-Nested Edge Detection","volume":"125","author":"Xie","year":"2015","journal-title":"Int. J. Comput. Vis."},{"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":"Mohammed, A.A., and Umaashankar, V. (2018, January 19\u201322). Effectiveness of Hierarchical Softmax in Large Scale Classification Tasks. Proceedings of the 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India.","DOI":"10.1109\/ICACCI.2018.8554637"},{"key":"ref_38","unstructured":"Arribas, J.I., Cid-Sueiro, J., Adali, T., and Figueiras-Vidal, A.R. (1999, January 25). Neural architectures for parametric estimation of a posteriori probabilities by constrained conditional density functions. Proceedings of the 1999 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing IX, Madison, WI, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., 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_40","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_41","unstructured":"Buhmann, J.M. (2010). The Binormal Assumption on Precision-Recall Curves. Int. Conf. Pattern Recognit."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1109\/TPAMI.2007.70740","article-title":"Efficient Multiclass ROC Approximation by Decomposition via Confusion Matrix Perturbation Analysis","volume":"30","author":"Landgrebe","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1501\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:26:54Z","timestamp":1760174814000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1501"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,8]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["rs12091501"],"URL":"https:\/\/doi.org\/10.3390\/rs12091501","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,8]]}}}