Volume 11 Issue 1
Feb.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(1): 157-167
KANG Jian, WANG Zhirui, ZHU Ruoxin, et al. Supervised contrastive learning regularized high-resolution synthetic aperture radar building footprint generation[J]. Journal of Radars, 2022, 11(1): 157–167. doi: 10.12000/JR21124
Citation: KANG Jian, WANG Zhirui, ZHU Ruoxin, et al. Supervised contrastive learning regularized high-resolution synthetic aperture radar building footprint generation[J]. Journal of Radars, 2022, 11(1): 157–167. doi: 10.12000/JR21124
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Supervised Contrastive Learning Regularized High-resolution Synthetic Aperture Radar Building Footprint Generation

doi: 10.12000/JR21124
  • 1.

    School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China

  • 2.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

  • 3.

    State Key Laboratory of Geo-Information Engineering, Xi’an Research Institute of Surveying and Mapping, Xi’an 710054, China

  • 4.

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China

  • 5.

    Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

Funds:  The National Natural Science Foundation of China (62101371, 62076241), Jiangsu Province Science Foundation for Youths (BK20210707)
More Information
  • Corresponding author: WANG Zhirui, zhirui1990@126.com
  • Received Date: 2021-09-07
  • Accepted Date: 2021-11-15
  • Rev Recd Date: 2021-11-12
    • Available Online: 2021-11-17
  • Publish Date: 2021-12-03
    Abstract
  • Over the recent years, high-resolution Synthetic-Aperture Radar (SAR) images have been widely applied for intelligent interpretation of urban mapping, change detection, etc. Different from optical images, the acquisition approach and object geometry of SAR images have limited the interpretation performances of the existing deep-learning methods. This paper proposes a novel building footprint generation method for high-resolution SAR images. This method is based on supervised contrastive learning regularization, which aims to increase the similarities between intra-class pixels and diversities of interclass pixels. This increase will make the deep learning models focus on distinguishing building and nonbuilding pixels in latent space, and improve the classification accuracy. Based on public SpaceNet6 data, the proposed method can improve the segmentation performance by 1% compared to the other state-of-the-art methods. This improvement validates the effectiveness of the proposed method on real data. This method can be used for building segmentation in urban areas with complex scene background. Moreover, the proposed method can be extended for other types of land-cover segmentation using SAR images.

     

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    • References(27)
  • [1]
    徐丰, 王海鹏, 金亚秋. 深度学习在SAR目标识别与地物分类中的应用[J]. 雷达学报, 2017, 6(2): 136–148. doi: 10.12000/JR16130

    XU Feng, WANG Haipeng, and JIN Yaqiu. Deep learning as applied in SAR target recognition and terrain classification[J]. Journal of Radars, 2017, 6(2): 136–148. doi: 10.12000/JR16130
    [2]
    王雪松, 陈思伟. 合成孔径雷达极化成像解译识别技术的进展与展望[J]. 雷达学报, 2020, 9(2): 259–276. doi: 10.12000/JR19109

    WANG Xuesong and CHEN Siwei. Polarimetric synthetic aperture radar interpretation and recognition: Advances and perspectives[J]. Journal of Radars, 2020, 9(2): 259–276. doi: 10.12000/JR19109
    [3]
    丁赤飚, 仇晓兰, 徐丰, 等. 合成孔径雷达三维成像——从层析、阵列到微波视觉[J]. 雷达学报, 2019, 8(6): 693–709. doi: 10.12000/JR19090

    DING Chibiao, QIU Xiaolan, XU Feng, et al. Synthetic aperture radar three-dimensional imaging—from TomoSAR and array InSAR to microwave vision[J]. Journal of Radars, 2019, 8(6): 693–709. doi: 10.12000/JR19090
    [4]
    李宁, 牛世林. 基于局部超分辨重建的高精度SAR图像水域分割方法[J]. 雷达学报, 2020, 9(1): 174–184. doi: 10.12000/JR19096

    LI Ning and NIU Shilin. High-precision water segmentation from synthetic aperture radar images based on local super-resolution restoration technology[J]. Journal of Radars, 2020, 9(1): 174–184. doi: 10.12000/JR19096
    [5]
    ZHAO Lingjun, ZHOU Xiaoguang, and KUANG Gangyao. Building detection from urban SAR image using building characteristics and contextual information[J]. EURASIP Journal on Advances in Signal Processing, 2013, 2013: 56. doi: 10.1186/1687-6180-2013-56
    [6]
    TUPIN F and ROUX M. Detection of building outlines based on the fusion of SAR and optical features[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2003, 58(1/2): 71–82.
    [7]
    XU Feng and JIN Yaqin. Automatic reconstruction of building objects from multiaspect meter-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(7): 2336–2353. doi: 10.1109/TGRS.2007.896614
    [8]
    MICHAELSEN E, SOERGEL U, and THOENNESSEN U. Perceptual grouping for automatic detection of man-made structures in high-resolution SAR data[J]. Pattern Recognition Letters, 2006, 27(4): 218–225. doi: 10.1016/j.patrec.2005.08.002
    [9]
    FERRO A, BRUNNER D, and BRUZZONE L. Automatic detection and reconstruction of building radar footprints from single VHR SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 51(2): 935–952.
    [10]
    ZHANG Fengli, SHAO Yun, ZHANG Xiao, et al. Building L-shape footprint extraction from high resolution SAR image[C]. 2011 Joint Urban Remote Sensing Event, Munich, Germany, 2011: 273–276.
    [11]
    WANG Yinghua, TUPIN F, HAN Chongzhao, et al. Building detection from high resolution PolSAR data by combining region and edge information[C]. IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA, 2009: IV–153.
    [12]
    GOODFELLOW I, BENGIO Y, and COURVILLE A. Deep Learning[M]. Cambridge: MIT press, 2016: 1–800.
    [13]
    WANG Xiaying, CAVIGELLI L, EGGIMANN M, et al. Hr-SAR-NET: A deep neural network for urban scene segmentation from high-resolution SAR data[C]. 2020 IEEE Sensors Applications Symposium (SAS), Kuala Lumpur, Malaysia, 2020: 1–6.
    [14]
    杜康宁, 邓云凯, 王宇, 等. 基于多层神经网络的中分辨SAR图像时间序列建筑区域提取[J]. 雷达学报, 2016, 5(4): 410–418. doi: 10.12000/JR16060

    DU Kangning, DENG Yunkai, WANG Yu, et al. Medium resolution SAR image time-series built-up area extraction based on multilayer neural network[J]. Journal of Radars, 2016, 5(4): 410–418. doi: 10.12000/JR16060
    [15]
    SHERMEYER J, HOGAN D, BROWN J, et al. SpaceNet 6: Multi-sensor all weather mapping dataset[C]. The 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, USA, 2020: 768–777.
    [16]
    SHAHZAD M, MAURER M, FRAUNDORFER F, et al. Buildings detection in VHR SAR images using fully convolution neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(2): 1100–1116. doi: 10.1109/TGRS.2018.2864716
    [17]
    JING Hao, SUN Xian, WANG Zhirui, et al. Fine building segmentation in high-resolution SAR images via selective pyramid dilated network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 6608–6623. doi: 10.1109/JSTARS.2021.3076085
    [18]
    CHEN Jiankun, QIU Xiaolan, DING Chibiao, et al. CVCMFF Net: Complex-valued convolutional and multifeature fusion network for building semantic segmentation of InSAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, in press. doi: 10.1109/TGRS.2021.3068124
    [19]
    SUN Yao, HUA Yuansheng, MOU Lichao, et al. CG-Net: Conditional GIS-Aware network for individual building segmentation in VHR SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, in press. doi: 10.1109/TGRS.2020.3043089
    [20]
    CHEN L, ZHU Yukun, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]. The European Conference on Computer Vision, Munich, Germany, 2018: 833–851.
    [21]
    CHEN Ting, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations[C]. The 37th International Conference on Machine Learning, Virtual Event, 2020: 1597–1607.
    [22]
    HE Kaiming, FAN Haoqi, WU Yuxin, et al. Momentum contrast for unsupervised visual representation learning[C]. The 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 9726–9735.
    [23]
    KHOSLA P, TETERWAK P, WANG Chen, et al. Supervised contrastive learning[C]. Advances in Neural Information Processing Systems, Virtual, 2020: 18661–18673.
    [24]
    LIU Shikun, ZHI Shuaifeng, JOHNS E, et al. Bootstrapping semantic segmentation with regional contrast[J]. arXiv: 2104.04465, 2021.
    [25]
    LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]. The 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2999–3007.
    [26]
    RONNEBERGER O, FISCHER P, and BROX T. U-Net: Convolutional networks for biomedical image segmentation[C]. 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015: 234–241.
    [27]
    HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. The 2016 IEEE Conference on Computer Vision and Pattern Recognition, Caesars Palace, Las Vegas, USA, 2016: 770–778.
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