Optics and Precision Engineering, Volume. 33, Issue 10, 1638(2025)

Optical remote sensing road extraction network with directional guidance and topological awareness

Yuebo MENG1,2、*, Xinyu HUANG1,2, Shilong SU1,2, and Heng WANG1,2
Author Affiliations
  • 1College of Information and Control Engineering,Xi'an University of Architecture and Technology,Xi'an70055,China
  • 2Key Laboratory of Construction Robots for Higher Education in Shaanxi Province, Xi'an710055,China
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    References(40)

    [1] DAI J G, WANG Y, DU Y et al. Development and prospect of road extraction method for optical remote sensing image[J]. Journal of Remote Sensing, 24, 804-823(2020).

         戴激光, 王杨, 杜阳. 光学遥感影像道路提取的方法综述[J]. 遥感学报, 24, 804-823(2020).

    [2] SHAO Y Z, GUO B X, HU X Y et al. Application of a fast linear feature detector to road extraction from remotely sensed imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4, 626-631(2010).

    [3] LAPTEV I, MAYER H, LINDEBERG T et al. Automatic extraction of roads from aerial images based on scale space and snakes[J]. Machine Vision and Applications, 12, 23-31(2000).

    [4] WEGNER J D, MONTOYA-ZEGARRA J A, SCHINDLER K. A Higher-Order CRF Model for Road Network Extraction[C]. OR, 1698-1705(2013).

    [5] SHELHAMER E, LONG J, DARRELL T. Fully Convolutional Networks for Semantic Segmentation[C], 640-651(2017).

    [6] BADRINARAYANAN V, KENDALL A, CIPOLLA R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

    [7] RONNEBERGER O, FISCHER P, BROX T[M]. U-Net: Convolutional Networks for Biomedical Image Segmentation, 234-241(2015).

    [8] CHAURASIA A, CULURCIELLO E. LinkNet: Exploiting encoder Representations for Efficient Semantic Segmentation[C], 1-4(2017).

    [9] XU Y Y, XIE Z, FENG Y X et al. Road extraction from high-resolution remote sensing imagery using deep learning[J]. Remote Sensing, 10, 1461(2018).

    [10] ZHOU L C, ZHANG C, WU M. D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction[C], 192-1924(2018).

    [11] YANG Z G, ZHOU D X, YANG Y et al. TransRoadNet: a novel road extraction method for remote sensing images via combining high-level semantic feature and context[J]. IEEE Geoscience and Remote Sensing Letters, 19, 6509505(2022).

    [12] KAMPFFMEYER M, DONG N Q, LIANG X D et al. ConnNet: a long-range relation-aware pixel-connectivity network for salient segmentation[J]. IEEE Transactions on Image Processing(2018).

    [13] MEI J, LI R J, GAO W et al. CoANet: connectivity attention network for road extraction from satellite imagery[J]. IEEE Transactions on Image Processing, 30, 8540-8552(2021).

    [14] JIE Y S, HE H Y, XING K et al. MECA-net: a MultiScale feature encoding and long-range context-aware network for road extraction from remote sensing images[J]. Remote Sensing, 14, 5342(2022).

    [15] 吴强强, 王帅, 王彪. 空间信息感知语义分割模型的高分辨率遥感影像道路提取[J]. 遥感学报, 26, 1872-1885(2022).

         WU Q Q, WANG S, WANG B et al. Road extraction method of high-resolution remote sensing image on the basis of the spatial information perception semantic segmentation model[J]. National Remote Sensing Bulletin, 26, 1872-1885(2022).

    [16] HOU Y W, LIU Z Y, ZHANG T et al. C-UNet: complement UNet for remote sensing road extraction[J]. Sensors, 21, 2153(2021).

    [17] PANG Y W, LI Y Z, SHEN J B et al. Towards bridging semantic gap to improve semantic segmentation[C], 4229-4238(2019).

    [18] MOSINSKA A, MARQUEZ-NEILA P, KOZINSKI M et al. Beyond the pixel-wise loss for topology-aware delineation[C], 3136-3145(2018).

    [19] SHIT S, PAETZOLD J C, SEKUBOYINA A et al. clDice-A novel topology-preserving loss function for tubular structure segmentation[C], 20, 16560-16569(2021).

    [20] HU X, LI F, SAMARAS D et al. Topology-preserving deep image segmentation[J]. Advances in neural information processing systems.

    [21] WONG C C, VONG C M. Persistent homology based graph convolution network for fine-grained 3d shape segmentation[C], 7078-7087(2021).

    [22] FU J, LIU J, JIANG J et al. Scene segmentation with dual relation-aware attention network[J]. IEEE Transactions on Neural Networks and Learning Systems, 32, 2547-2560(2021).

    [23] CARLSSON G. Topology and data[J]. Bulletin of the American Mathematical Society, 46, 255-308(2009).

    [24] EDELSBRUNNER, LETSCHER, ZOMORODIAN, EDELSBRUNNER, LETSCHER, ZOMORODIAN, EDELSBRUNNER, LETSCHER, ZOMORODIAN. Topological persistence and simplification[J]. Discrete & Computational Geometry, 28, 511-533(2002).

    [25] COHEN-STEINER D, EDELSBRUNNER H, HARER J et al. Lipschitz functions have lp-stable persistence[J]. Foundations of Computational Mathematics, 10, 127-139(2010).

    [26] TAHA A A, HANBURY A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool[J]. BMC Medical Imaging, 15, 29(2015).

    [27] ZHAO H S, SHI J P, QI X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

    [28] DAI L, ZHANG G Y, ZHANG R T. RADANet: road augmented deformable attention network for road extraction from complex high-resolution remote-sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 5602213(2023).

    [29] YANG M X, YUAN Y, LIU G C. SDUNet: Road extraction via spatial enhanced and densely connected UNet[J]. Pattern Recognition, 126, 108549(2022).

    [30] LUO L, WANG J X, CHEN S B et al. BDTNet: road extraction by bi-direction transformer from remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 19, 2505605(2022).

    [31] CHEN J, YANG L B, WANG H et al. Road extraction from high-resolution remote sensing images via local and global context reasoning[J]. Remote Sensing, 15, 4177(2023).

    [32] ZHU X H, HUANG X H, CAO W J et al. Road extraction from remote sensing imagery with spatial attention based on swin transformer[J]. Remote Sensing, 16, 1183(2024).

    [33] XIONG Y Q, LI L, YUAN D et al. CFRNet: road extraction in remote sensing images based on cascade fusion network[J]. IEEE Geoscience and Remote Sensing Letters, 21, 6011705(2024).

    [34] LI J, LIU Y, ZHANG Y D et al. Cascaded attention DenseUNet (CADUNet) for road extraction from very-high-resolution images[J]. ISPRS International Journal of Geo-Information, 10, 329(2021).

    [35] ZHOU G D, CHEN W T, GUI Q S et al. Split depth-wise separable graph-convolution network for road extraction in complex environments from high-resolution remote-sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5614115(2021).

    [36] WANG Y, PENG Y X, LI W et al. DDU-Net: dual-decoder-U-net for road extraction using high-resolution remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 4412612(2022).

    [37] LI S F, LIAO C, DING Y L et al. Cascaded residual attention enhanced road extraction from remote sensing images[J]. ISPRS International Journal of Geo-Information, 11, 9(2022).

    [38] ZHANG R X, ZHU W, LI Y K et al. D-FusionNet: road extraction from remote sensing images using dilated convolutional block[J]. GIScience & Remote Sensing, 60, 2270806(2023).

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    Yuebo MENG, Xinyu HUANG, Shilong SU, Heng WANG. Optical remote sensing road extraction network with directional guidance and topological awareness[J]. Optics and Precision Engineering, 2025, 33(10): 1638

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    Paper Information

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    Received: Nov. 10, 2024

    Accepted: --

    Published Online: Jul. 23, 2025

    The Author Email: Yuebo MENG (mengyuebo@163.com)

    DOI:10.37188/OPE.20253310.1638

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