留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于图像分割和密度聚类的遥感动目标分块提取

林翊钧 吴凤鸽 赵军锁

林翊钧, 吴凤鸽, 赵军锁等 . 基于图像分割和密度聚类的遥感动目标分块提取[J]. 北京航空航天大学学报, 2018, 44(12): 2510-2520. doi: 10.13700/j.bh.1001-5965.2018.0354
引用本文: 林翊钧, 吴凤鸽, 赵军锁等 . 基于图像分割和密度聚类的遥感动目标分块提取[J]. 北京航空航天大学学报, 2018, 44(12): 2510-2520. doi: 10.13700/j.bh.1001-5965.2018.0354
LIN Yijun, WU Fengge, ZHAO Junsuoet al. Image segmentation and density clustering for moving object patches extraction in remote sensing image[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(12): 2510-2520. doi: 10.13700/j.bh.1001-5965.2018.0354(in Chinese)
Citation: LIN Yijun, WU Fengge, ZHAO Junsuoet al. Image segmentation and density clustering for moving object patches extraction in remote sensing image[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(12): 2510-2520. doi: 10.13700/j.bh.1001-5965.2018.0354(in Chinese)

基于图像分割和密度聚类的遥感动目标分块提取

doi: 10.13700/j.bh.1001-5965.2018.0354
详细信息
    作者简介:

    林翊钧  男, 博士研究生。主要研究方向:智能信息处理

    吴凤鸽  女, 博士, 副研究员。主要研究方向:智能信息处理

    赵军锁  男, 博士, 研究员。主要研究方向:智能信息处理

    通讯作者:

    吴凤鸽, E-mail: fengge@iscas.ac.cn

  • 中图分类号: V19;TP751.1

Image segmentation and density clustering for moving object patches extraction in remote sensing image

More Information
  • 摘要:

    大幅宽遥感图像的动目标检测研究中,卷积神经网络虽然取得了显著效果,但算法存在目标搜索空间庞大、模型极其消耗时间及计算资源的问题,因此本文从目标区域预筛选的角度给出了针对性优化方法。首先,基于局部误差处理的策略,改进了现有的图像分割算法来粗糙地提取动目标可能存在的区域。然后,以相邻区域合并、减少总数量和面积为目的,设计了一种基于空间约束的密度聚类算法——SC-DBSCAN,其以分治思想来降低问题的规模,通过空间尺寸的先验约束自适应地将数据划分为多个相互独立的簇,并针对簇的复杂程度选择相应的合并策略,在复杂簇中,考虑到合并结果与对象遍历顺序相关,易陷入局部最优,引入基于模拟退火思想的随机扰动有效提升了输出的图像块质量。最终,通过减少模型推断次数及避免目标的重复检测,显著地改进动目标检测的整体效率。

     

  • 图 1  模块结构

    Figure 1.  Modular structure

    图 2  面向图像块的局部不精确分割效果比较

    Figure 2.  Comparsion of patch-oriented local imprecise segmentation effect

    图 3  基于SC-DBSCAN算法的分簇结果

    Figure 3.  Clustering results by SC-DBSCAN algorithm

    图 4  wpafb2009数据集

    Figure 4.  wpafb2009 dataset

    图 5  低阈值分割和OTSU阈值分割的效果比较

    Figure 5.  Comparsion of low-threshold segmentation and OTSU threshold segmentation

    图 6  相邻帧光照变化处理比较

    Figure 6.  Comparison of illumination in neighbor frames

    图 7  不同算法在区域1、2的合并结果

    Figure 7.  Merging results by different algorithms in Region 1 and Region 2

    图 8  参数γ对最终图像块数量和面积的影响

    Figure 8.  Impact of parameter γ on final image patches' amount and area

    表  1  候选运动目标质量比较

    Table  1.   Qualitative comparison in candidate moving objects

    %
    算法 精度 召回率
    GMM 92.72 83.53
    S-3frame 89.06 75.76
    N-3frame 91.37 87.73
    本文算法 90.68 93.30
    下载: 导出CSV

    表  2  最终图像块质量比较

    Table  2.   Qualitative comparison of final image patches

    算法 精度/% 召回率/% 数量比 面积比 重复比
    基线 90.98 93.96 1.2241 1.2241 1.4804
    HC 98.60 94.25 0.3876 1.1049 0.0843
    DBSCAN 98.59 94.15 0.4018 1.1053 0.0999
    本文算法 98.79 94.34 0.3818 1.0586 0.0756
    下载: 导出CSV

    表  3  参数γ对结果的影响

    Table  3.   Impact of parameter γ on result

    权重γ 数量比 面积比
    -0.05 1.1963 1.2008
    -0.02 0.7792 0.9626
    0 0.6108 0.9299
    0.03 0.4947 0.952
    0.06 0.4352 0.9921
    0.1 0.3818 1.0586
    0.3 0.2973 1.341
    0.55 0.2953 1.356
    下载: 导出CSV
  • [1] 徐伟, 金光, 王家骐.吉林一号轻型高分辨率遥感卫星光学成像技术[J].光学精密工程, 2017, 25(8):1969-1978. http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201708001

    XU W, JIN G, WANG J Q.Optical imaging technology of JL-1 lightweight high resolution multispectral remote sensing satellite[J].Optics & Precision Engineering, 2017, 25(8):1969-1978(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201708001
    [2] MARK K, LUIS G, CHRIS S.Real-time tracking of low-resolution vehicles for wide-area persistent surveillance[C]//Workshop on Application of Computer Vision.Piscataway, NJ: IEEE Press, 2013: 441-448.
    [3] IMRAN S, MUBARAK S.Multiframe many-many point correspondence for vehicle tracking in high density wide area aerial videos[J].International Journal of Computer Vision, 2013, 104(2):198-219. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=fbf20305e03481f0ddbc2298bd3ea72c
    [4] LALONDE R, ZHANG D, SHAH M.ClusterNet: Detecting small objects in large scenes by exploiting spatio-temporal information[C]//Computer Vision and Pattern Recognition (CVPR).Piscataway, NJ: IEEE Press, 2018: 4003-4012.
    [5] GAO F, ZHANG Y, WANG J, et al.Visual attention model based vehicle target detection in synthetic aperture radar images:A novel approach[J].Cognitive Computation, 2015, 7(4):434-444. doi: 10.1007/s12559-014-9312-x
    [6] GAO F, MA F, ZHANG Y, et al.Biologically inspired progre-ssive enhancement target detection from heavy cluttered SAR images[J].Cognitive Computation, 2016, 8(5):955-966. doi: 10.1007/s12559-016-9405-9
    [7] BLASCH E, CHEN G, LING H.Vehicle classification in WAMI imagery using deep network[C]//Sensors and Systems for Space Applications.New York: SPIE, 2016: 9838-9846.
    [8] REN S, HE K, GIRSHICK R, et al.Faster R-CNN: Towards real-time object detection with region proposal networks[C]//International Conference on Neural Information Processing Systems.Cambridge: MIT Press, 2015: 91-99.
    [9] REDMON J, DIVVALA S, GIRSHICK R, et al.You only look once: Unified, real-time object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2016: 779-788.
    [10] LIU W, ANGUELOV D, ERHAN D, et al.SSD: Single shot multibox detector[C]//European Conference on Computer Vision (ECCV).Berlin: Springer, 2016: 21-37.
    [11] DAI J, LI Y, HE K, et al.R-FCN: Object detection via region-based fully convolutional networks[C]//Advances in Neural Information Processing Systems.Piscataway, NJ: IEEE Press, 2016: 379-387.
    [12] SOMMER L W, TEUTSCH M, SCHUCHERT T, et al.A survey moving object detection for wide area motion imagery[C]//Winter Conference on Applications of Computer Vision (WACV).Piscataway, NJ: IEEE Press, 2016: 1-9.
    [13] 罗群明, 施霖.图像拼接方法综述[J].传感器与微系统, 2017, 36(12):4-6. http://d.old.wanfangdata.com.cn/Periodical/cgqjs201712002

    LUO Q M, SHI L.Review on image stitching methods[J].Transducer & Microsystem Technologies, 2017, 36(12):4-6(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/cgqjs201712002
    [14] THOMAS P, MATTHEW A.Detecting and tracking all moving objects in wide-area aerial video[C]//Computer Vision and Pattern Recognition Workshops.Piscataway, NJ: IEEE Press, 2012: 15-22.
    [15] ZIVKOVIC Z.Improved adaptive Gaussian mixture model for background subtraction[C]//International Conference on Pa-ttern Recognition.Piscataway, NJ: IEEE Press, 2004: 28-31.
    [16] ESTER M, KRIEGEL H P, SANDER J, et al.Density-based spatial clustering of applications with noise[C]//International Conference on Knowledge Discovery and Data Mining.Reston: AIAA, 1996: 226-231.
    [17] 张小朋, 钱海忠, 岳辉丽, 等.基于模拟退火的空间聚类算法[J].测绘科学技术学报, 2010, 27(4):306-309. doi: 10.3969/j.issn.1673-6338.2010.04.018

    ZHANG X P, QIAN H Z, YUE H L, et al.Simulated-annealing based spatial clustering algorithm[J].Journal of Geomatics Science & Technology, 2010, 27(4):306-309(in Chinese). doi: 10.3969/j.issn.1673-6338.2010.04.018
    [18] KIRKPATRICK S.Optimization by simulated annealing:Quantitative studies[J].Journal of Statistical Physics, 1984, 34(5-6):975-986. doi: 10.1007/BF01009452
    [19] AFRL.Wright-patterson air force base (wpafb) dataset[EB/OL].[2017-12-10].http://sdms.afrl.af.mil/index.php?collection=wpafb2009.
    [20] BASHARAT A, TUREK M, XU Y L, et al.Real-time multi-target tracking at 210 megapixels/second in wide area motion imagery[C]//IEEE Winter Conference on Applications of Computer Vision.Piscataway, NJ: IEEE Press, 2014: 839-846.
    [21] GAO F, YOU J, WANG J, et al.A novel target detection method for SAR images based on shadow proposal and saliency analysis[J].Neurocomputing, 2017, 267:220-231. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0dfe94ea6f5d81f985fccc7f369da1da
    [22] GAO F, MA F, WANG J, et al.Visual saliency modeling for river detection in high-resolution SAR imagery[J].IEEE Access, 2018, 6:1000-1014. doi: 10.1109/ACCESS.2017.2777444
  • 加载中
图(8) / 表(3)
计量
  • 文章访问数:  379
  • HTML全文浏览量:  4
  • PDF下载量:  442
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-06-13
  • 录用日期:  2018-08-21
  • 刊出日期:  2018-12-20

目录

    /

    返回文章
    返回
    常见问答