An effective dual encoder network with a feature attention large kernel for building extraction

References

• Alshehhi R, Marpu PR, Woon WL, Mura MD. 2017. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS J Photogramm Remote Sens. 130:139–149. doi: 10.1016/j.isprsjprs.2017.05.002.
   Web of Science ®Google Scholar
• Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M. 2022. Swin-UNET: UNET-like pure Transformer for medical image segmentation//European conference on computer vision. Cham Switzerland: Springer Nature Switzerland; p. 205–218.
   Google Scholar
• Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L,Yuille A, Zhou Y. 2021. Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306.
   Google Scholar
• Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. 2018. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell. 40(4):834–848. doi: 10.1109/TPAMI.2017.2699184.
   PubMed Web of Science ®Google Scholar
• Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai XH, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al. 2021. An image is worth 16 × 16words: transformers for image recognition at scale[2022-03-26]. https://arxiv.org/pdf/2010.11929.pdf.
   Google Scholar
• Fan C, Jiang H. 2016. Fine building extraction from world view-2 imagery using random forest. Geospat Inform. 14(01):58–62+5. https://kns.cnki.net/kcms/detail/42.1692.p.20160129.1533.034.html
   Google Scholar
• Gong M, Liu T, Zhang M, Zhang Q, Lu D, Zheng H, Jiang F. 2023. Context–content collaborative network for building extraction from high-resolution imagery. Knowledge-Based Systems. 263:110283. doi: 10.1016/j.knosys.2023.110283.
   Web of Science ®Google Scholar
• Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. 2014. Generative adversarial nets. Advances in Neural Information Processing Systems; Dec 8–13; Montreal, Quebec, Canada, 2014, 27.
   Google Scholar
• Han K, Xiao A, Wu E, Guo J, Xu C, Wang Y. 2021. Transformer in transformer. Advances in Neural Information Processing Systems; Dec 6–14; NeurIPS 2021 is a Virtual-only Conference, 2021, 34: 15908–15919.
   Google Scholar
• He Z, Ding H, An B. 2022. High-resolution remote sensing image building extraction with hole convolution e-UNET algorithm. Acta Geodaetic Cartograph Sin. 51 (03):457–467. https://link.cnki.net/urlid/11.2089.P.20211215.1136.012
   Google Scholar
• Hou F. 2023. Research on high-resolution remote sensing image retrieval technology based on deep learning. Beijing, China: Beijing University of Technology. doi: 10.26935/d.cnki.gbjgu.2021.000320.
   Google Scholar
• Huang Q. 2022. Extraction of building roofs from Remote Sensing Images Based on Extreme Random Trees. Ganzhou, China: Jiangxi University of Science and Technology. doi: 10.27176/d.cnki.gnfyc.2021.000169.
   Google Scholar
• Ji S, Wei S. 2019. Convolutional neural network and open-source dataset methods for extracting buildings from remote sensing images. Acta Geodaetic Cartograph Sin. 48(4):448–459. https://kns.cnki.net/kcms2/article/abstract?v=LeTZRn7a1NLSgYpy1cJzzWwyrdsSc__eDCzwQlm54CC4KYXFjPDeNrtIQ3TvN-SSgDMqUg7f3_blXI2jAuzI_s_dXHQU9V6v9n8PcbK_Hk7omjE9tpKGZhU8yaOyYACzpFp8szx4-xnhlsUqH-puEHBq_v97Vo5L&uniplatform=NZKPT&language=CHS
   Google Scholar
• Jia X, Bartlett J, Zhang T, Lu W, Qiu Z, Duan J. 2022. U-Net vs transformer: is u-net outdated in medical image registration? International Conference on Medical Image Computing and Computer Assisted Intervention - Workshop on Machine Learning in Medical Imaging (MICCAI-MLMI) Sep 18–22; Singapore, 2022:151–160.
   Google Scholar
• Kendall A, Gal Y, Cipolla R. 2017. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122.
   Google Scholar
• Li X, Huang L, Zhu J, Sun Y, Yang W. 2023. Edge-enhanced EDU-Net for building extraction in remote sensing images. Remote Sens Inform. 38(02):134–141. doi: 10.20091/j.cnki.1000-3177.2023.02.018.
   Google Scholar
• Li L, Liang J, Weng M, Zhu H. 2018. A multiple-feature reuse network to extract buildings from remote sensing imagery. Remote Sens. 10(9):1350. doi: 10.3390/rs10091350.
   Web of Science ®Google Scholar
• Li R, Zheng S, Duan C, Su J, Wang L, Atkinson PM. 2021. Multi attention network for semantic segmentation of fine-resolution remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, Town-Piscataway, United States, 2021, 60: 1–13.
   Google Scholar
• Liao WY. 2024. Extraction method of building roofs from high spatial resolution remote sensing images based on deep learning. Ganzhou, China: Jiangxi University of Science and Technology. doi: 10.27176/d.cnki.gnfyc.2023.000899.
   Google Scholar
• Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B. 2021. Swin transformer: hierarchical vision transformer using shifted windows. Proceedings of the IEEE. /CVF International Conference on Computer Vision, Oct 11–17; Montreal, BC, Canada. 2021:10012–10022.
   Google Scholar
• Liu J, Liu Z, Li F. 2021. Classification of urban building clusters based on remote sensing images. J Nat Dis. 30(06):61–66. doi: 10.13577/j.jnd.2021.0607.
   Google Scholar
• Ronneberger O, Fischer P, Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 2015: 18th international conference, Oct 5–9, Munich, Germany, 2015, proceedings, part III 18. Springer International Publishing, 2015: 234–241.
   Google Scholar
• Su J, Liu Z, Zhang J, Sheng VS, Song Y, Zhu Y, Liu Y. 2021. DV-Net: accurate liver vessel segmentation via dense connection model with D-BCE loss function. Knowledge-Based Syst. 232:107471. doi: 10.1016/j.knosys.2021.107471.
   Web of Science ®Google Scholar
• Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. 2017. Attention is all you need. Advances in Neural Information Processing Systems, Dec 4–9; Long Beach, CA, USA, 2017, 30.
   Google Scholar
• Wang H, Cao P, Wang J, Zaiane O. 2021. UCtransnet: rethinking the skip connections in U-Net from a channel-wise perspective with Transformer. arXiv:2109.04335.
   Google Scholar
• Wang P, Chen P, Yuan Y, Liu D, Huang Z, Hou X, Cottrell G. 2018. Understanding convolution for semantic segmentation. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018: 1451–1460.
   Google Scholar
• Wang L, Fang S, Meng X, Li R. 2022. Building extraction with vision transformer. IEEE Trans Geosci Remote Sensing. 60:1–11. doi: 10.1109/TGRS.2022.3186634.
   Web of Science ®Google Scholar
• Wu K, Zheng Y, Chen Y, Zeng L, Zhang J. 2021. Dataset of typical urban buildings in China. China Sci Data. 6(01):182–190.
   Google Scholar
• Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P. 2021. Segformer: simple and efficient design for semantic segmentation with transformers[C]. Advances in Neural Information Processing Systems, Dec 6–14; NeurIPS 2021 is a Virtual-only Conference, 2021, 34:12077–12090.
   Google Scholar
• Yang H. 2022. Research on information extraction from high-resolution remote sensing images using a deep learning model with visual attention mechanism. Wuhan, China: Wuhan University. doi: 10.27379/d.cnki.gwhdu.2019.001342.
   Google Scholar
• Yang MY, Kumaar S, Lyu Y, Nex F. 2021. Real-time semantic segmentation with context aggregation network. ISPRS J Photogramm Remote Sens. 178:124–134. doi: 10.1016/j.isprsjprs.2021.06.006.
   Web of Science ®Google Scholar
• Zhang Y, Guo W, Wu C. 2023. Fast building extraction in remote sensing images through fusion of CNN and transformer. Optics Precision Eng. 31(11):1700–1709. doi: 10.37188/OPE.20233111.1700.
   Google Scholar
• Zhao H, Shi J, Qi X, Wang X, Jia J. 2017. Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul 21–26; Honolulu, HI, USA, 2017: 2881–2890.
   Google Scholar
• Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, Fu Y, Feng J, Xiang T, Torr PHS, et al. 2021. Rethinking semantic segmentation from a sequence-to-sequence perspective with Transformers. //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. p. 6881–6890.
   Google Scholar
• Zhou K, Yang Y, Zhang Y, Miao R, Yang Y. 2021. Comprehensive review of land use classification methods for optical remote sensing images. Sci Technol Eng. 21 (32):13603–13613. https://kns.cnki.net/kcms2/article/abstract?v=LeTZRn7a1NJPJHVmv1Ptf4W6uZQeYbH3s2WBC1D7ACueebM4N3YP_s22x3WnqEkRz14wSD-VnW8RONU_VTABlDWwxjW-j8_VbMM8WkQjgn9ttnUOJqydE_QAZXgun9cPIMqR_rbfRqMc1bAQWNmZ9_xspz1byUvK&uniplatform=NZKPT&language=CHS
   Google Scholar
• Zhou W. 2022. Research on remote sensing image retrieval based on deep learning features. Wuhan, China: Wuhan University, doi: 10.27379/d.cnki.gwhdu.2019.002310.
   Google Scholar
• Zhou L, Mao N. 2023. Comprehensive review on visual transformer for recognition tasks. J Image Graph. 28 (10):2969–3003. https://kns.cnki.net/kcms2/article/abstract?v=LeTZRn7a1NKXxRqwB1PnFUcsb8iLX5lT_RD-rmA-OzJUm5_FVfHisn4zZc48eo0DSWxs3ffD-lxuTxVDqf0RAd1PGeZ8VBO3_D27ZkaVIEhceBm3BbSqNNy70VhfApwbNjk8wmovYTmja2X8de4BxiHXbzKn_Gl4&uniplatform=NZKPT&language=CHS
   Google Scholar