合成孔径雷达三维成像中的视觉语义浅析

胡占义

胡占义. 合成孔径雷达三维成像中的视觉语义浅析[J]. 雷达学报, 2022, 11(1): 20–26. doi: 10.12000/JR21149
引用本文: 胡占义. 合成孔径雷达三维成像中的视觉语义浅析[J]. 雷达学报, 2022, 11(1): 20–26. doi: 10.12000/JR21149
HU Zhanyi. A note on visual semantics in SAR 3D imaging[J]. Journal of Radars, 2022, 11(1): 20–26. doi: 10.12000/JR21149
Citation: HU Zhanyi. A note on visual semantics in SAR 3D imaging[J]. Journal of Radars, 2022, 11(1): 20–26. doi: 10.12000/JR21149

合成孔径雷达三维成像中的视觉语义浅析

doi: 10.12000/JR21149
基金项目: 国家自然科学基金(61991423, 61772444)
详细信息
    作者简介:

    胡占义(1961–),男,山西繁峙人,博士,研究员。1985年于北方工业大学自动化系获学士学位,1993年于比利时列日大学工学院获博士学位。1993年至今在中国科学院自动化研究所模式识别国家重点实验室工作。长期从事计算机视觉和生物视觉研究,个人网页:http://vision.ia.ac.cn/zh/faculty/zyhu/index.html

    通讯作者:

    胡占义 huzy@nlpr.ia.ac.cn

  • 责任主编:丁赤飚 Corresponding Editor: DING Chibiao
  • 中图分类号: TP391

A Note on Visual Semantics in SAR 3D Imaging

Funds: The National Natural Science Foundation of China (61991423, 61772444)
More Information
  • 摘要: “合成孔径雷达微波视觉三维成像”,从概念上说,旨在将“视觉语义”引入到合成孔径雷达的成像模型中,以期提高三维成像的质量。对层析合成孔径雷达(TomoSAR)来说, “视觉语义”的引入可望有效减少TomoSAR所需的观测次数。然而,什么是“视觉语义”?从视觉感知的途径看, “单眼”和“双眼”均可以从场景感知三维结构信息;从场景内容看,不同的人对同一幅图像会有不同感受;从视觉神经加工机理看,三维信息加工和二维信息加工也存在一些本质差异。另外,人类视觉感知普遍存在错觉(illusion)现象。那么,到底什么类型的“视觉语义信息”可望在计算的层次上有助于微波三维成像呢?如何借鉴计算机视觉的理论和方法来提取微波三维成像中有用的“视觉语义”信息呢?该文对这些问题进行了一些初步探讨。

     

  • 图  1  视觉腹部通道和背部通道。腹部通道主要负责物体视觉,背部通道主要负责空间视觉

    Figure  1.  Visual ventral pathway and dorsal pathway: Ventral pathway is mainly for object vision, dorsal pathway for spatial vision

    图  2  从线画图可以产生三维感觉[1]

    Figure  2.  Human could perceive 3D shape from line drawing[1]

    图  3  图中两个人身高感觉存在明显差异

    Figure  3.  The Ames room illusion. Two women in the picture have similar heights, but perceived very differently

    图  4  从图(a)可以感知到船的一些三维结构信息;图(b)可以感知到桥的一些三维结构

    Figure  4.  3D ship structural information could be perceived from (a); Bridge 3D shape could be clearly perceived from (b)

    图  5  TomoSAR迭代处理框架

    Figure  5.  TomoSAR iterative framework

    图  6  TomoSAR的伪多尺度处理框架

    Figure  6.  TomoSAR pseudo-multi-scale framework

  • [1] MARR D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information[M]. New York: W. H. Freeman, 1982.
    [2] OHZAWA I, DEANGELIS G C, and FREEMAN R D. Stereoscopic depth discrimination in the visual cortex: Neurons ideally suited as disparity detectors[J]. Science, 1990, 249(4972): 1037–1041. doi: 10.1126/science.2396096
    [3] HAEFNER R M and CUMMING B G. Adaptation to natural binocular disparities in primate V1 explained by a generalized energy model[J]. Neuron, 2008, 57(1): 147–158. doi: 10.1016/j.neuron.2007.10.042
    [4] BRENNER E and SMEETS J B. Depth Perception[M]. WIXTED J. Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience: Sensation, Perception, and Attention. 4th ed. New York: John Wiley & Sons, 2018: 385–414.
    [5] 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
    [6] DEL CAMPO G M, NANNINI M, and REIGBER A. Statistical regularization for enhanced TomoSAR imaging[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 1567–1589. doi: 10.1109/JSTARS.2020.2970595
    [7] ZHU Xiaoxiang and BAMLER R. Tomographic SAR inversion by L1-norm regularization—The compressive sensing approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(10): 3839–3846. doi: 10.1109/TGRS.2010.2048117
    [8] RAMBOUR C, DENIS L, TUPIN F, et al. Introducing spatial regularization in SAR tomography reconstruction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 8600–8617. doi: 10.1109/TGRS.2019.2921756
    [9] VIOLA P and JONES M. Rapid object detection using a boosted cascade of simple features[C]. 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, USA, 2001: I–I. doi: 10.1109/CVPR.2001.990517.
    [10] FISCHLER M A and BOLLES R C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381–395. doi: 10.1145/358669.358692
    [11] 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
    [12] LINDEBERG T. Scale-space[M]. WAH B W. Wiley Encyclopedia of Computer Science and Engineering. Hoboken: John Wiley & Sons, Inc. , 2008: 2495–2504. doi: 10.1002/9780470050118.ecse609.
    [13] CUI Hainan, GAO Xiang, SHNE Shuhan, et al. HSfM: Hybrid Structure-from-motion[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 2393–2402. doi: 10.1109/CVPR.2017.257.
    [14] LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91–110. doi: 10.1023/B:VISI.0000029664.99615.94
    [15] KOLMOGOROV V and ZABIN R. What energy functions can be minimized via graph cuts?[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(2): 147–159. doi: 10.1109/TPAMI.2004.1262177
    [16] COSTANTE G, CIARFUGLIA T A, and BIONDI F. Towards monocular digital elevation model (DEM) estimation by convolutional neural networks - Application on synthetic aperture radar images[J]. arXiv: 1803.05387, 2018: 1–6.
    [17] BUDILLON A, JOHNSY A C, SCHIRINZI G, et al. SAR tomography based on deep learning[C]. 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019: 3625–3628. doi: 10.1109/IGARSS.2019.8900616.
    [18] WU Chunxiao, ZHANG Zenghui, CHEN Longyong, et al. Super-resolution for MIMO array SAR 3-D imaging based on compressive sensing and deep neural network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 3109–3124. doi: 10.1109/JSTARS.2020.3000760
    [19] XU Dan, RICCI E, OUYANG Wanli, et al. Multi-scale continuous CRFs as sequential deep networks for monocular depth estimation[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 161–169. doi: 10.1109/CVPR.2017.25.
    [20] WANG Yuanyuan and ZHU Xiaoxiang. SAR tomography via nonlinear blind scatterer separation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(7): 5751–5763. doi: 10.1109/TGRS.2020.3022209
    [21] FORNARO G, VERDE S, REALE D, et al. CAESAR: An approach based on covariance matrix decomposition to improve multibaseline-multitemporal interferometric SAR processing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 2050–2065. doi: 10.1109/TGRS.2014.2352853
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出版历程
  • 收稿日期:  2021-10-09
  • 修回日期:  2021-11-20
  • 网络出版日期:  2021-12-10
  • 刊出日期:  2022-02-28

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