{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:05:12Z","timestamp":1770833112229,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T00:00:00Z","timestamp":1635897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Advance Research Project of Civil Space Technology","award":["D040402"],"award-info":[{"award-number":["D040402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Semantic segmentation for high-resolution remote-sensing imagery (HRRSI) has become increasingly popular in machine vision in recent years. Most of the state-of-the-art methods for semantic segmentation of HRRSI usually emphasize the strong learning ability of deep convolutional neural network to model the contextual relationship in the image, which takes too much consideration on every pixel in images and subsequently causes the problem of overlearning. Annotation errors and easily confused features can also lead to the confusion problem while using the pixel-based methods. Therefore, we propose a new semantic segmentation network\u2014the region-enhancing network (RE-Net)\u2014to emphasize the regional information instead of pixels to solve the above problems. RE-Net introduces the regional information into the base network, to enhance the regional integrity of images and thus reduce misclassification. Specifically, the regional context learning procedure (RCLP) can learn the context relationship from the perspective of regions. The region correcting procedure (RCP) uses the pixel aggregation feature to recalibrate the pixel features in each region. In addition, another simple intra-network multi-scale attention module is introduced to select features at different scales by the size of the region. A large number of comparative experiments on four different public datasets demonstrate that the proposed RE-Net performs better than most of the state-of-the-art ones.<\/jats:p>","DOI":"10.3390\/s21217316","type":"journal-article","created":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T21:57:49Z","timestamp":1635976669000},"page":"7316","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Region-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3128-9914","authenticated-orcid":false,"given":"Bo","family":"Zhong","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang","family":"Du","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minghao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aixia","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjun","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,3]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.isprsjprs.2017.11.014","article-title":"Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images","volume":"138","author":"Maboudi","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2016, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_4","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017, December 05). Rethinking Atrous Convolution for Semantic Image Segmentation. Available online: https:\/\/arxiv.org\/abs\/1706.05587."},{"key":"ref_5","unstructured":"Tao, A., Sapra, K., and Catanzaro, B. (2020, May 21). Hierarchical Multi-Scale Attention for Semantic Segmentation. Available online: https:\/\/arxiv.org\/abs\/2005.10821."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, August 22). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Available online: https:\/\/openaccess.thecvf.com\/content_ECCV_2018\/html\/Liang-Chieh_Chen_Encoder-Decoder_with_Atrous_ECCV_2018_paper.html.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"He, J.J., Deng, Z.Y., Zhou, L., Wang, Y.L., Qiao, Y., and Soc, I.C. (2019, January 15\u201320). Adaptive Pyramid Context Network for Semantic Segmentation. Proceedings of the 2019 IEEE\/Cvf Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00770"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yuan, J., Deng, Z., Wang, S., and Luo, Z. (2020, January 1\u20135). Multi Receptive Field Network for Semantic Segmentation. Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093264"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Wang, Y., Li, Y., Bao, Y., Tang, J., and Lu, H. (2019, January 27\u201328). Adaptive Context Network for Scene Parsing. Proceedings of the 2019 Ieee\/Cvf International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00685"},{"key":"ref_10","unstructured":"Chen, L.C., Collins, M.D., Zhu, Y.K., Papandreou, G., Zoph, B., Schroff, F., Adam, H., and Shlens, J. (2018, September 11). Searching for Efficient Multi-Scale Architectures for Dense Image Prediction. Available online: https:\/\/arxiv.org\/abs\/1809.04184."},{"key":"ref_11","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., Mcdonagh, S., Hammerla, N.Y., and Kainz, B. (2018, May 20). Attention U-Net: Learning Where to Look for the Pancreas. Available online: https:\/\/arxiv.org\/abs\/1804.03999."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Takikawa, T., Acuna, D., Jampani, V., and Fidler, S. (2019, January 27\u201328). Gated-SCNN: Gated Shape CNNs for Semantic Segmentation. Proceedings of the 2019 IEEE\/Cvf International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00533"},{"key":"ref_13","unstructured":"Krhenb\u00fchl, P., and Koltun, V.J.C.A.I. (2012, October 20). Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials. Available online: http:\/\/papers.nips.cc\/paper\/4296-efficient-inference-in-fullyconnected-crfs-with-gaussian-edge-potentials.pdf."},{"key":"ref_14","unstructured":"Islam, M.A., Naha, S., Rochan, M., Bruce, N., and Wang, Y. (2017, March 01). Label Refinement Network for Coarse-to-Fine Semantic Segmentation. Available online: https:\/\/arxiv.org\/abs\/1703.00551."},{"key":"ref_15","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Navab","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention, Pt Iii"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., and Agrawal, A. (2018, January 18\u201323). Context Encoding for Semantic Segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00747"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_18","unstructured":"Yuan, Y., and Wang, J. (2018, September 04). OCNet: Object Context Network for Scene Parsing. Available online: https:\/\/arxiv.org\/abs\/1809.00916."},{"key":"ref_19","unstructured":"Yuan, Y., Chen, X., and Wang, J.J.S. (2021, April 30). Object-Contextual Representations for Semantic Segmentation. Available online: https:\/\/arxiv.org\/abs\/1909.11065."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1007\/978-3-030-01240-3_17","article-title":"PSANet: Point-wise Spatial Attention Network for Scene Parsing","volume":"Volume 11213","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014Eccv 2018, Pt Ix"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., and Liu, W. (2019, January 27\u201328). CCNet: Criss-Cross Attention for Semantic Segmentation. Proceedings of the 2019 IEEE\/Cvf International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00069"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, F., Chen, Y., Li, Z., Hong, Z., and Ding, E. (2019, January 27\u201328). ACFNet: Attentional Class Feature Network for Semantic Segmentation. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00690"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Xu, M., Bai, S., Huang, T., and Bai, X. (2019, January 27\u201328). Asymmetric Non-Local Neural Networks for Semantic Segmentation. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00068"},{"key":"ref_24","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 2019 IEEE\/Cvf International Conference on Computer Vision Workshops, Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, H., Wang, C., Xie, J., and Soc, I.C. (2019, January 15\u201320). Co-occurrent Features in Semantic Segmentation. Proceedings of the 2019 IEEE\/Cvf Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00064"},{"key":"ref_26","unstructured":"Niu, R. (2020, September 15). HMANet: Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images. Available online: https:\/\/arxiv.org\/abs\/2001.02870."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.isprsjprs.2019.11.006","article-title":"Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation","volume":"159","author":"Mi","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","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 2018 IEEE\/Cvf Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Lin, Z., Bidart, R., Hu, X., and Wong, A. (2020, April 01). Squeeze-and-Attention Networks for Semantic Segmentation. Available online: https:\/\/openaccess.thecvf.com\/content_CVPR_2020\/html\/Zhong_Squeeze-and-Attention_Networks_for_Semantic_Segmentation_CVPR_2020_paper.html.","DOI":"10.1109\/CVPR42600.2020.01308"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Yi, Y., Jiang, W., Wei, X., and Yuille, A.L. (2016, January 27\u201330). Attention to Scale: Scale-Aware Semantic Image Segmentation. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.396"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, M., Yu, K., Chi, Z., Li, Z., and Yang, K. (2018, January 18\u201322). DenseASPP for Semantic Segmentation in Street Scenes. Proceedings of the CVPR, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00388"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.11.009","article-title":"Classification with an edge: Improving semantic with boundary detection","volume":"135","author":"Marmanis","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Xie, J., Chen, X., and Wang, J. (2020, January 23\u201328). SegFix: Model-Agnostic Boundary Refinement for Segmentation. In Proceeding of the 16th European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58610-2_29"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fieraru, M., Khoreva, A., Pishchulin, L., and Schiele, B. (2018, January 18\u201323). Learning to Refine Human Pose Estimation. Proceedings of the 2018 Ieee\/Cvf Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00058"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gidaris, S., and Komodakis, N. (2016, January 21\u201326). Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.760"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, K., Hariharan, B., and Malik, J. (2016, January 27\u201330). Iterative Instance Segmentation. Proceedings of the Computer Vision & Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.398"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kuo, W.C., Angelova, A., Malik, J., and Lin, T.Y. (2019, January 27\u201328). ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors. Proceedings of the 2019 Ieee\/Cvf International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00930"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2019.07.007","article-title":"TreeUNet: Adaptive Tree convolutional neural networks for subdecimeter aerial image segmentation","volume":"156","author":"Yue","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7557","DOI":"10.1109\/TGRS.2020.2979552","article-title":"Relation Matters: Relational Context-Aware Fully Convolutional Network for Semantic Segmentation of High-Resolution Aerial Images","volume":"58","author":"Mou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"1766","DOI":"10.1109\/LGRS.2019.2907009","article-title":"End-to-End DSM Fusion Networks for Semantic Segmentation in High-Resolution Aerial Images","volume":"16","author":"Cao","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","unstructured":"(2011, March 07). The Vaihingen Data Set Was Provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [Cramer, 2010]. Available online: https:\/\/github.com\/nshaud\/DeepNetsForEO."},{"key":"ref_44","unstructured":"(2011, March 07). The Postdam Data Set Was Provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [Cramer, 2010]. Available online: http:\/\/www.ifp.uni-stuttgart.de\/dgpf\/DKEP-Allg.html."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., and Raskar, R. (2018, May 17). DeepGlobe 2018; A Challenge to Parse the Earth Through Satellite Images. Available online: https:\/\/openaccess.thecvf.com\/content_cvpr_2018_workshops\/w4\/html\/Demir_DeepGlobe_2018_A_CVPR_2018_paper.html.","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"ref_46","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., and Wang, J. (2019, April 09). High-Resolution Representations for Labeling Pixels and Regions. Available online: https:\/\/arxiv.org\/abs\/1904.04514."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., Wang, J.D., and Soc, I.C. (2019, January 15\u201320). Deep High-Resolution Representation Learning for Human Pose Estimation. Proceedings of the 2019 IEEE\/Cvf Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TGRS.2016.2616585","article-title":"Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks","volume":"55","author":"Volpi","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","unstructured":"Park, J., Woo, S., Lee, J.Y., and Kweon, I.S. (2018, July 18). BAM: Bottleneck Attention Module. Available online: https:\/\/arxiv.org\/abs\/1807.06514."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Woo, S.H., Park, J., Lee, J.Y., and Kweon, I.S. (2018, October 06). CBAM: Convolutional Block Attention Module. Available online: https:\/\/openaccess.thecvf.com\/content_ECCV_2018\/html\/Sanghyun_Woo_Convolutional_Block_Attention_ECCV_2018_paper.html.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chen, Y.P., Rohrbach, M., Yan, Z.C., Yan, S.C., Feng, J.S., Kalantidis, Y., and Soc, I.C. (2019, January 15\u201320). Graph-Based Global Reasoning Networks. Proceedings of the 2019 IEEE\/Cvf Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00052"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"7503","DOI":"10.1109\/TGRS.2019.2913861","article-title":"Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks","volume":"57","author":"Nogueira","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7316\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:25:13Z","timestamp":1760167513000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7316"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,3]]},"references-count":52,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217316"],"URL":"https:\/\/doi.org\/10.3390\/s21217316","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,3]]}}}