Optics and Precision Engineering, Volume. 31, Issue 22, 3371(2023)

Lightweight multi-scale difference network for remote sensing building extraction

Guoyan LI1... Haimiao WU1, Chunhua DONG2,* and Yi LIU1 |Show fewer author(s)
Author Affiliations
  • 1School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin300384, China
  • 2School of Geology and Mapping, Tianjin Chengjian University, Tianjin300384, China
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    References(24)

    [1] Y WANG, X Q ZENG, X H LIAO et al. B-FGC-net: a building extraction network from high resolution remote sensing imagery. Remote Sensing, 14, 269(2022).

    [2] H HOSSEINPOUR, F SAMADZADEGAN, FD JAVAN. CMGFNet: a deep cross-modal gated fusion network for building extraction from very high-resolution remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 184, 96-115(2022).

    [3] X Y JIN, C H DAVIS. Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information. EURASIP Journal on Advances in Signal Processing, 1-11(2005).

    [4] [4] 岳照溪, 潘琛, 郭功举. 基于深度学习的高分辨率光学卫星遥感影像建筑物变化检测方法[J]. 测绘科学与工程, 2021(2): 30-38.YUEZ X, PANC, GUOG J. A change detection method for high resolution optical satellite remote sensing image based on deep learning[J]. Geomatics Science and Engineering, 2021(2): 30-38.(in Chinese)

    [5] E SHELHAMER, J LONG, T DARRELL. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

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

    [7] O RONNEBERGER, P FISCHER, T BROX. U-Net Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science, 234-241(2015).

    [8] [8] 王舒洋, 慕晓冬, 杨东方, 等. 融合高阶信息的遥感影像建筑物自动提取[J]. 光学 精密工程, 2019, 27(11): 2474-2483. doi: 10.3788/ope.20192711.2474WANGS Y, MUX D, YANGD F, et al. High-order statistics integration method for automatic building extraction of remote sensing images[J]. Opt. Precision Eng. , 2019, 27(11)2474-2483.(in Chinese). doi: 10.3788/ope.20192711.2474

    [9] [9] 陈凯强, 高鑫, 闫梦龙, 等. 基于编解码网络的航空影像像素级建筑物提取[J]. 遥感学报, 2020, 24(9): 1134-1142. doi: 10.11834/jrs.20209056CHENK Q, GAOX, YANM L, et al. Building extraction in pixel level from aerial imagery with a deep encoder-decoder network[J]. Journal of Remote Sensing, 2020, 24(9): 1134-1142.(in Chinese). doi: 10.11834/jrs.20209056

    [10] J J MA, L L WU, X TANG et al. Building extraction of aerial images by a global and multi-scale encoder-decoder network. Remote Sensing, 12, 2350(2020).

    [11] M Y SHI, J GAO. Research on high altitude remote sensing building segmentation based on improved u-net algorithm. Instrumentation, 8, 47-54(2021).

    [12] [12] 许正森, 管海燕, 彭代锋, 等. 高分辨率遥感影像建筑物提取的注意力胶囊网络算法[J]. 遥感学报, 2022, 26(8): 1636-1649. doi: 10.11834/jrs.20221577XUZ S, GUANH Y, PENGD F, et al. A dual-attention capsule network for building extraction from high-resolution remote sensing imagery[J]. Journal of Remote Sensing, 2022, 26(8): 1636-1649.(in Chinese). doi: 10.11834/jrs.20221577

    [13] Z X ZHANG, Q J LIU, Y H WANG. Road extraction by deep residual U-net. IEEE Geoscience and Remote Sensing Letters, 15, 749-753(2018).

    [14] [14] 徐胜军, 张若暄, 孟月波, 等. 融合分形几何特征Resnet遥感图像建筑物分割[J]. 光学 精密工程, 2022, 30(16)2006-2020. doi: 10.37188/OPE.20223016.2006XUS J, ZHANGR X, MENGY B, et al. Fusion of fractal geometric features Resnet remote sensing image building segmentation[J]. Opt. Precision Eng. , 2022, 30(16)2006-2020.(in Chinese). doi: 10.37188/OPE.20223016.2006

    [15] [15] 罗松强, 李浩, 陈仁喜. 多尺度特征增强的ResUNet+遥感影像建筑物提取[J]. 激光与光电子学进展, 2022, 59(8): 0828007. doi: 10.3788/LOP202259.0828007LUOS Q, LIH, CHENR X. Building extraction of Remote sensing images using ResUNnet+ with enhanced multiscale features[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0828007.(in Chinese). doi: 10.3788/LOP202259.0828007

    [16] [16] 徐胜军, 欧阳朴衍, 郭学源, 等. 多尺度特征融合空洞卷积ResNet遥感图像建筑物分割[J]. 光学 精密工程, 2020, 28(7): 1588-1599. doi: 10.37188/OPE.20202807.1588XUS J, OUYANGP Y, GUOX Y, et al. Building segmentation in remote sensing image based on multiscale-feature fusion dilated convolution resnet[J]. Opt. Precision Eng., 2020, 28(7): 1588-1599.(in Chinese). doi: 10.37188/OPE.20202807.1588

    [17] J B LIN, W P JING, H B SONG et al. ESFNet: efficient network for building extraction from high-resolution aerial images. IEEE Access, 7, 54285-54294(2019).

    [19] K M HE, X Y ZHANG, S Q REN et al. Deep residual learning for image recognition, 770-778(27).

    [21] A RAZA, H HUO, T FANG. EUNet-CD: efficient UNet for change detection of very high-resolution remote sensing images. IEEE Geoscience and Remote Sensing Letters, 19, 1-5(2022).

    [22] K SHAHEED, A MAO, I QURESHI et al. Finger-vein presentation attack detection using depthwise separable convolution neural network. Expert Systems with Applications, 198, 116786(2022).

    [23] S H WANG, X Y WANG, X GUO. Advanced face mask detection model using hybrid dilation convolution based method. Journal of Software Engineering and Applications, 16, 1-19(2023).

    [24] [24] 季顺平, 魏世清. 遥感影像建筑物提取的卷积神经元网络与开源数据集方法[J]. 测绘学报, 2019, 48(4): 448-459. doi: 10.11947/j.AGCS.2019.20180206JIS P, WEIS Q. Building extraction via convolutional neural networks from an open remote sensing building dataset[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(4): 448-459.(in Chinese). doi: 10.11947/j.AGCS.2019.20180206

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    Guoyan LI, Haimiao WU, Chunhua DONG, Yi LIU. Lightweight multi-scale difference network for remote sensing building extraction[J]. Optics and Precision Engineering, 2023, 31(22): 3371

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    Paper Information

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    Received: Jun. 4, 2023

    Accepted: --

    Published Online: Dec. 29, 2023

    The Author Email: DONG Chunhua (dch@tcu.edu.cn)

    DOI:10.37188/OPE.20233122.3371

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