{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:42:54Z","timestamp":1775608974660,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:00:00Z","timestamp":1670371200000},"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":["62176085"],"award-info":[{"award-number":["62176085"]}],"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":["61673359"],"award-info":[{"award-number":["61673359"]}],"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":["KJ2020A0658"],"award-info":[{"award-number":["KJ2020A0658"]}],"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":["KJ2021ZD0118"],"award-info":[{"award-number":["KJ2021ZD0118"]}],"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":["20RC13"],"award-info":[{"award-number":["20RC13"]}],"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":["20ZR03ZDA"],"award-info":[{"award-number":["20ZR03ZDA"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Scientific Research Foundation of the Education Department of Province Anhui","award":["62176085"],"award-info":[{"award-number":["62176085"]}]},{"name":"Key Scientific Research Foundation of the Education Department of Province Anhui","award":["61673359"],"award-info":[{"award-number":["61673359"]}]},{"name":"Key Scientific Research Foundation of the Education Department of Province Anhui","award":["KJ2020A0658"],"award-info":[{"award-number":["KJ2020A0658"]}]},{"name":"Key Scientific Research Foundation of the Education Department of Province Anhui","award":["KJ2021ZD0118"],"award-info":[{"award-number":["KJ2021ZD0118"]}]},{"name":"Key Scientific Research Foundation of the Education Department of Province Anhui","award":["20RC13"],"award-info":[{"award-number":["20RC13"]}]},{"name":"Key Scientific Research Foundation of the Education Department of Province Anhui","award":["20ZR03ZDA"],"award-info":[{"award-number":["20ZR03ZDA"]}]},{"name":"University Natural Sciences Research Project of Province","award":["62176085"],"award-info":[{"award-number":["62176085"]}]},{"name":"University Natural Sciences Research Project of Province","award":["61673359"],"award-info":[{"award-number":["61673359"]}]},{"name":"University Natural Sciences Research Project of Province","award":["KJ2020A0658"],"award-info":[{"award-number":["KJ2020A0658"]}]},{"name":"University Natural Sciences Research Project of Province","award":["KJ2021ZD0118"],"award-info":[{"award-number":["KJ2021ZD0118"]}]},{"name":"University Natural Sciences Research Project of Province","award":["20RC13"],"award-info":[{"award-number":["20RC13"]}]},{"name":"University Natural Sciences Research Project of Province","award":["20ZR03ZDA"],"award-info":[{"award-number":["20ZR03ZDA"]}]},{"name":"Hefei University Talent Research Funding","award":["62176085"],"award-info":[{"award-number":["62176085"]}]},{"name":"Hefei University Talent Research Funding","award":["61673359"],"award-info":[{"award-number":["61673359"]}]},{"name":"Hefei University Talent Research Funding","award":["KJ2020A0658"],"award-info":[{"award-number":["KJ2020A0658"]}]},{"name":"Hefei University Talent Research Funding","award":["KJ2021ZD0118"],"award-info":[{"award-number":["KJ2021ZD0118"]}]},{"name":"Hefei University Talent Research Funding","award":["20RC13"],"award-info":[{"award-number":["20RC13"]}]},{"name":"Hefei University Talent Research Funding","award":["20ZR03ZDA"],"award-info":[{"award-number":["20ZR03ZDA"]}]},{"name":"Hefei University Scientific Research Development Funding","award":["62176085"],"award-info":[{"award-number":["62176085"]}]},{"name":"Hefei University Scientific Research Development Funding","award":["61673359"],"award-info":[{"award-number":["61673359"]}]},{"name":"Hefei University Scientific Research Development Funding","award":["KJ2020A0658"],"award-info":[{"award-number":["KJ2020A0658"]}]},{"name":"Hefei University Scientific Research Development Funding","award":["KJ2021ZD0118"],"award-info":[{"award-number":["KJ2021ZD0118"]}]},{"name":"Hefei University Scientific Research Development Funding","award":["20RC13"],"award-info":[{"award-number":["20RC13"]}]},{"name":"Hefei University Scientific Research Development Funding","award":["20ZR03ZDA"],"award-info":[{"award-number":["20ZR03ZDA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional neural networks have attracted much attention for their use in the semantic segmentation of remote sensing imagery. The effectiveness of semantic segmentation of remote sensing images is significantly influenced by contextual information extraction. The traditional convolutional neural network is constrained by the size of the convolution kernel and mainly concentrates on local contextual information. We suggest a new lightweight global context semantic segmentation network, LightFGCNet, to fully utilize the global context data and to further reduce the model parameters. It uses an encoder\u2013decoder architecture and gradually combines feature information from adjacent encoder blocks during the decoding upsampling stage, allowing the network to better extract global context information. Considering that the frequent merging of feature information produces a significant quantity of redundant noise, we build a unique and lightweight parallel channel spatial attention module (PCSAM) for a few critical contextual features. Additionally, we design a multi-scale fusion module (MSFM) to acquire multi-scale feature target information. We conduct comprehensive experiments on the two well-known datasets ISPRS Vaihingen and WHU Building. The findings demonstrate that our suggested strategy can efficiently decrease the number of parameters. Separately, the number of parameters and FLOPs are 3.12 M and 23.5 G, respectively, and the mIoU and IoU of our model on the two datasets are 70.45% and 89.87%, respectively, which is significantly better than what the conventional convolutional neural networks for semantic segmentation can deliver.<\/jats:p>","DOI":"10.3390\/rs14246193","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T04:38:54Z","timestamp":1670387934000},"page":"6193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["LightFGCNet: A Lightweight and Focusing on Global Context Information Semantic Segmentation Network for Remote Sensing Imagery"],"prefix":"10.3390","volume":"14","author":[{"given":"Yan","family":"Chen","sequence":"first","affiliation":[{"name":"Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9450-3415","authenticated-orcid":false,"given":"Wenxiang","family":"Jiang","sequence":"additional","affiliation":[{"name":"Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Mengyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Menglei","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Big Data and Information Engineering, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Thomas","family":"Weise","sequence":"additional","affiliation":[{"name":"Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Xiaofeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Big Data and Information Engineering, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Ming","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Big Data and Information Engineering, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Lixiang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Big Data and Information Engineering, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3806-3993","authenticated-orcid":false,"given":"Xinlu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Big Data and Information Engineering, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Big Data and Information Engineering, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105909","DOI":"10.1016\/j.compag.2020.105909","article-title":"State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter","volume":"180","author":"Zhang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","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 (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_3","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 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_4","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_5","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_6","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_7","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 15th European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_8","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xu, Z., Zhang, W., Zhang, T., Yang, Z., and Li, J. (2021). Efficient Transformer for Remote Sensing Image Segmentation. Remote Sens., 13.","DOI":"10.3390\/rs13183585"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/TIP.2020.3042065","article-title":"CGNet: A Light-Weight Context Guided Network for Semantic Segmentation","volume":"30","author":"Wu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xu, Z., Zhang, W., Zhang, T., and Li, J. (2021). HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13122290"},{"key":"ref_12","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 32nd IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: A survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, W., Zhu, X., Sun, R., He, J., Li, R., Shen, X., and Yu, B. (2020). Tensor Low-Rank Reconstruction for Semantic Segmentation. arXiv.","DOI":"10.1007\/978-3-030-58520-4_4"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, M., Yu, K., Zhang, C., Li, Z., and Yang, K. (2018, January 18\u201323). DenseASPP for Semantic Segmentation in Street Scenes. Proceedings of the 31st IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00388"},{"key":"ref_16","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 30th IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"014515","DOI":"10.1117\/1.JRS.16.014515","article-title":"SRANet: Semantic relation aware network for semantic segmentation of remote sensing images","volume":"16","author":"Gao","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhang, Y., Liu, S., Shi, J., Loy, C.C., Lin, D., and Jia, J. (2018, January 8\u201314). PSANet: Point-wise Spatial Attention Network for Scene Parsing. Proceedings of the 15th European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01240-3_17"},{"key":"ref_19","unstructured":"Liu, S., De Mello, S., Gu, J., Zhong, G., Yang, M.H., and Kautz, J. (2017, January 4\u20139). Learning Affinity via Spatial Propagation Networks. Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xu, D., Arnab, A., and Torr, P.H.S. (2020, January 14\u201319). Dynamic Graph Message Passing Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Online.","DOI":"10.1109\/CVPR42600.2020.00378"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3002","DOI":"10.1080\/01431161.2020.1856960","article-title":"Multiscale denoising autoencoder for improvement of target detection","volume":"42","author":"Sun","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 31st IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gao, Z., Xie, J., Wang, Q., and Li, P. (2019, January 15\u201320). Global second-order pooling convolutional networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00314"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Qin, Z., Zhang, P., Wu, F., and Li, X. (2021, January 11\u201317). FcaNet: Frequency Channel Attention Networks. Proceedings of the 18th IEEE\/CVF International Conference on Computer Vision (ICCV), Online.","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image Super-resolution Using Very Deep Residual Channel Attention Networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_27","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K. (2015, January 7\u201312). Spatial Transformer Networks. Proceedings of the 29th Annual Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_28","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., and Vedaldi, A. (2018, January 3\u20138). Gather-excite: Exploiting feature context in convolutional neural networks. Proceedings of the Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Liao, C., Hu, H., Li, H.F., Ge, X.M., Chen, M., Li, C.N., and Zhu, Q. (2021). Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction. Remote Sens., 13.","DOI":"10.3390\/rs13061049"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., and Sun, J. (2018, January 8\u201314). ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. Proceedings of the 15th European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_32","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for MobileNetV3. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, D., Hou, Q., Chen, Y., Feng, J., and Yan, S. (2020, January 23\u201328). Rethinking Bottleneck Structure for Efficient Mobile Network Design. Proceedings of the European Conference on Computer Vision (ECCV), Edinburgh, UK.","DOI":"10.1007\/978-3-030-58580-8_40"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, Z.Q., Lu, W., Cao, J.S., and Xie, G.Q. (2022). MKANet: An Efficient Network with Sobel Boundary Loss for Land-Cover Classification of Satellite Remote Sensing Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14184514"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8636973","DOI":"10.1155\/2022\/8636973","article-title":"Extraction of Impervious Surface from High-Resolution Remote Sensing Images Based on a Lightweight Convolutional Neural Network","volume":"2022","author":"Chen","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.isprsjprs.2022.06.008","article-title":"UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery","volume":"190","author":"Wang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5614812","DOI":"10.1109\/TGRS.2021.3131331","article-title":"A Lightweight Network for Building Extraction From Remote Sensing Images","volume":"60","author":"Huang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2172","DOI":"10.1109\/LGRS.2020.3012705","article-title":"MFALNet: A Multiscale Feature Aggregation Lightweight Network for Semantic Segmentation of High-Resolution Remote Sensing Images","volume":"18","author":"Lv","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2014, January 6\u201312). Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. Proceedings of the 13th European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10578-9_23"},{"key":"ref_41","unstructured":"(2022, November 21). ISPRS Vaihingen Dataset. Available online: https:\/\/www.isprs.org\/education\/benchmarks\/UrbanSemLab\/2d-sem-label-vaihingen.aspx."},{"key":"ref_42","first-page":"448","article-title":"Building extraction via convolutional neural networks from an open remote sensing building dataset","volume":"48","author":"Ji","year":"2019","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Woo, S.H., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the 15th European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yu, C., Gao, C., Wang, J., Yu, G., Shen, C., and Sang, N. (2020). BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation. arXiv.","DOI":"10.1007\/s11263-021-01515-2"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1109\/LGRS.2020.3011151","article-title":"SMAF-net: Sharing multiscale adversarial feature for high-resolution remote sensing imagery semantic segmentation","volume":"18","author":"Chen","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/LGRS.2020.2988294","article-title":"SCAttNet: Semantic Segmentation Network With Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images","volume":"18","author":"Li","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1080\/01431161.2021.2018147","article-title":"MF-Dfnet: A deep learning method for pixel-wise classification of very high-resolution remote sensing images","volume":"43","author":"Zhang","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yu, M., Zhang, W., Chen, X., Liu, Y., and Niu, J. (2022). An End-to-End Atrous Spatial Pyramid Pooling and Skip-Connections Generative Adversarial Segmentation Network for Building Extraction from High-Resolution Aerial Images. Appl. Sci., 12.","DOI":"10.3390\/app12105151"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"54285","DOI":"10.1109\/ACCESS.2019.2912822","article-title":"ESFNet: Efficient Network for Building Extraction From High-Resolution Aerial images","volume":"7","author":"Lin","year":"2019","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"993961","DOI":"10.3389\/fpls.2022.993961","article-title":"Cropland encroachment detection via dual attention and multi-loss based building extraction in remote sensing images","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chen, J.Z., Zhang, D.J., Wu, Y.Q., Chen, Y.L., and Yan, X.H. (2022). A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14092276"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6193\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:35:34Z","timestamp":1760146534000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,7]]},"references-count":51,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246193"],"URL":"https:\/\/doi.org\/10.3390\/rs14246193","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,7]]}}}