Volume 11 Issue 1
Feb.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2022  >  11(1): 144-156
WANG Wei, XU Huarong, WEI Hanyu, et al. Progressive building facade detection for regularizing array InSAR point clouds[J]. Journal of Radars, 2022, 11(1): 144–156. doi: 10.12000/JR21177
Citation: WANG Wei, XU Huarong, WEI Hanyu, et al. Progressive building facade detection for regularizing array InSAR point clouds[J]. Journal of Radars, 2022, 11(1): 144–156. doi: 10.12000/JR21177
shu

Progressive Building Facade Detection for Regularizing Array InSAR Point Clouds

DOI: 10.12000/JR21177
  • 1.

    School of Network Engineering, Zhoukou Normal University, Zhoukou 466001, China

  • 2.

    School of Computer Information Engineering, Xiamen University of Technology, Xiamen 361024, China

  • 3.

    College of Mathematics and Statistics, Zhoukou Normal University, Zhoukou 466001, China

  • 4.

    Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

  • 5.

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China

Funds:  The National Natural Science Foundation of China (61991423, U1805264), The Foundation of the Lab of Space Optoelectronic Measurement & Perception (502K0019118), The Science and Technique Project of Henan (212102310397)
More Information
  • Corresponding author: DONG Qiulei, qldong@nlpr.ia.ac.cn
  • Received Date: 2021-11-11
  • Accepted Date: 2022-02-14
  • Rev Recd Date: 2022-01-28
    • Available Online: 2022-02-16
  • Publish Date: 2022-02-25
    Abstract
  • This study proposes a progressive building facade detection method based on structure priors to effectively detect building facades from massive array InSAR spatial points with noise. First, the proposed method projects the initial array of InSAR three-Dimensional (3D) points on the ground to produce connected regions that correspond to building facades and then progressively detects potential line segments in each connected region under the guidance of structure priors. Furthermore, the proposed method generates building facades according to the detected line segments and their corresponding 3D points. In this process, the line segment detection space of the current connected region is constructed based on line segments detected in its neighboring connected regions, thereby improving the overall efficiency and reliability of the current line segment detection. Experimental results confirm that the proposed method can efficiently produce more reliable building facades from a massive array of InSAR 3D points with noise, overcoming several difficulties (such as low efficiency and inferior reliability) encountered in traditional multi-model fitting methods.

     

    • FullText(HTML)
  • loading
    • References(27)
  • [1]
    JIAO Zekun, DING Chibiao, QIU Xiaolan, et al. Urban 3D imaging using airborne TomoSAR: Contextual information-based approach in the statistical way[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 170: 127–141. doi: 10.1016/j.isprsjprs.2020.10.013
    [2]
    曾涛, 温育涵, 王岩, 等. 合成孔径雷达参数化成像技术进展[J]. 雷达学报, 2021, 10(3): 327–341. doi: 10.12000/JR21004

    ZENG Tao, WEN Yuhan, WANG Yan, et al. Research progress on synthetic aperture radar parametric imaging methods[J]. Journal of Radars, 2021, 10(3): 327–341. doi: 10.12000/JR21004
    [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]
    WANG Yifan, WU Shihao, HUANG Hui, et al. Patch-based progressive 3D point set upsampling[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 5951–5960.
    [5]
    WANG Yuanyuan and ZHU Xiaoxiang. Automatic feature-based geometric fusion of multiview TomoSAR point clouds in urban area[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(3): 953–965. doi: 10.1109/JSTARS.2014.2361430
    [6]
    RAMBOUR C, DENIS L, TUPIN F, et al. Urban surface reconstruction in SAR tomography by graph-cuts[J]. Computer Vision and Image Understanding, 2019, 188: 102791. doi: 10.1016/j.cviu.2019.07.011
    [7]
    BARÁTH D and MATAS J. Progressive-X: Efficient, anytime, multi-model fitting algorithm[C]. 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), 2019: 3779–3787.
    [8]
    KLUGER F, BRACHMANN E, ACKERMANN H, et al. CONSAC: Robust multi-model fitting by conditional sample consensus[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 4633–4642.
    [9]
    WANG Wei and GAO Wei. Efficient multi-plane extraction from massive 3D points for modeling large-scale urban scenes[J]. The Visual Computer, 2019, 35(5): 625–638. doi: 10.1007/s00371-018-1492-z
    [10]
    FISCHLER M A and BOLLES R C. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography[M]. FISCHLER M A and FIRSCHEIN O. Readings in Computer Vision. Los Altos, USA: Morgan Kaufmann, 1987: 726–740.
    [11]
    ZULIANI M, KENNEY C S, and MANJUNATH B S. The multiRANSAC algorithm and its application to detect planar homographies[C]. IEEE International Conference on Image Processing 2005, Genova, Italy, 2005: III-153.
    [12]
    TOLDO R and FUSIELLO A. Robust multiple structures estimation with j-linkage[C]. The 10th European Conference on Computer Vision, Marseille, France, 2008: 537–547.
    [13]
    MAGRI L and FUSIELLO A. Robust multiple model fitting with preference analysis and low-rank approximation[C]. British Machine Vision Conference, Swansea, UK, 2015: 20.1–20.12.
    [14]
    MAGRI L and FUSIELLO A. T-linkage: A continuous relaxation of j-linkage for multi-model fitting[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 3954–3961.
    [15]
    MAGRI L and FUSIELLO A. Fitting multiple heterogeneous models by multi-class cascaded t-linkage[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 7452–7460.
    [16]
    ISACK H and BOYKOV Y. Energy-based geometric multi-model fitting[J]. International Journal of Computer Vision, 2012, 97(2): 123–147. doi: 10.1007/s11263-011-0474-7
    [17]
    PHAM T T, CHIN T J, YU Jin, et al. The random cluster model for robust geometric fitting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(8): 1658–1671. doi: 10.1109/TPAMI.2013.2296310
    [18]
    CHIN T J, YU Jin, and SUTER D. Accelerated hypothesis generation for multistructure data via preference analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(4): 625–638. doi: 10.1109/TPAMI.2011.169
    [19]
    BARATH D and MATAS J. Multi-class model fitting by energy minimization and mode-seeking[C]. The 15th European Conference on Computer Vision, Munich, Germany, 2018: 229–245.
    [20]
    BARATH D and MATAS J. Graph-Cut RANSAC[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Utah, USA, 2018: 6733–6741. doi: 10.1109/CVPR.2018.00704.
    [21]
    BRACHMANN E and ROTHER C. Neural-guided RANSAC: Learning where to sample model hypotheses[C]. 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), 2019: 4321–4330.
    [22]
    ZHU Xiaoxiang and SHAHZAD M. Façade reconstruction using multi-view spaceborne TomoSAR point clouds[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(6): 3541–3552. doi: 10.1109/TGRS.2013.2273619
    [23]
    SHAHZAD M and ZHU Xiaoxiang. Robust reconstruction of building facades for large areas using spaceborne TomoSAR point clouds[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(2): 752–769. doi: 10.1109/TGRS.2014.2327391
    [24]
    BALLARD D H. Generalizing the Hough transform to detect arbitrary shapes[J]. Pattern Recognition, 1981, 13(2): 111–122. doi: 10.1016/0031-3203(81)90009-1
    [25]
    VON GIOI R G, JAKUBOWICZ J, MOREL J M, et al. LSD: A fast line segment detector with a false detection control[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(4): 722–732. doi: 10.1109/TPAMI.2008.300
    [26]
    HUTTENLOCHER D P, KLANDERMAN G A, and RUCKLIDGE W J. Comparing images using the Hausdorff distance[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(9): 850–863. doi: 10.1109/34.232073
    [27]
    仇晓兰, 焦泽坤, 彭凌霄, 等. SARMV3D-1.0: SAR微波视觉三维成像数据集[J]. 雷达学报, 2021, 10(4): 485–498. doi: 10.12000/JR21112

    QIU Xiaolan, JIAO Zekun, PENG Lingxiao, et al. SARMV3D-1.0: Synthetic aperture radar microwave vision 3D imaging dataset[J]. Journal of Radars, 2021, 10(4): 485–498. doi: 10.12000/JR21112
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(1117) PDF downloads(123) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return