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光学卫星在轨动态场景实时匹配方法及试验

乔凯 黄石生 智喜洋 孙晅 赵明

乔凯, 黄石生, 智喜洋, 孙晅, 赵明. 光学卫星在轨动态场景实时匹配方法及试验[J]. 中国光学(中英文), 2019, 12(3): 575-586. doi: 10.3788/CO.20191203.0575
引用本文: 乔凯, 黄石生, 智喜洋, 孙晅, 赵明. 光学卫星在轨动态场景实时匹配方法及试验[J]. 中国光学(中英文), 2019, 12(3): 575-586. doi: 10.3788/CO.20191203.0575
QIAO Kai, HUANG Shi-sheng, ZHI Xi-yang, SUN Suan, ZHAO Ming. On-orbit dynamic scene real-time matching method and experiment of optical satellite[J]. Chinese Optics, 2019, 12(3): 575-586. doi: 10.3788/CO.20191203.0575
Citation: QIAO Kai, HUANG Shi-sheng, ZHI Xi-yang, SUN Suan, ZHAO Ming. On-orbit dynamic scene real-time matching method and experiment of optical satellite[J]. Chinese Optics, 2019, 12(3): 575-586. doi: 10.3788/CO.20191203.0575

光学卫星在轨动态场景实时匹配方法及试验

doi: 10.3788/CO.20191203.0575
基金项目: 

国家自然科学基金项目 61605035

详细信息
    作者简介:

    乔凯(1981—),男,山西省祁县人,助理研究员,2005年于哈尔滨工业大学获得硕士学士,主要从事光学遥感卫星论证、设计等方面的研究。E-mail:qk_lucky@sohu.com

  • 中图分类号: TP391;TP79

On-orbit dynamic scene real-time matching method and experiment of optical satellite

Funds: 

National Natural Science Foundation of China 61605035

More Information
  • 摘要: 针对目前在轨卫星图像动态范围偏窄、直方图集中、灰度层次不够丰富、暗场景图像细节分辨能力不强的问题,提出一种卫星在轨动态场景实时匹配方法。首先,研究云检测和基于直方图特性的大气程辐射预估方法,消除它们对场景高、低动态测量的影响,并结合测光相机与成像相机辐射响应关系的标定,通过测光相机最多2次拍摄地面场景,实现场景动态范围的实时测量;然后,针对地面场景动态范围通常超出相机动态的问题,设计并提出了基于高亮度和低亮度匹配的相机与场景动态范围匹配方案,同时给出了不同情况下相机在轨参数解算方法。最后,通过无人机飞行试验对匹配方法进行了试验验证,结果表明:利用该方法可根据实时拍摄的地面景物合理地设置相机积分级数和增益,实现相机与场景动态范围的最佳匹配,有效灰阶提升优于100%,信息熵提升优于40%。

     

  • 图 1  不同卫星图像直方图拉伸前后分布对比

    Figure 1.  Distribution comparison of different satellite image histograms before and after stretching

    图 2  含大气程辐射的实拍图像(a)及其直方图分布(b)

    Figure 2.  Actual image with atmospheric radiation(a) and corresponding histogram distribution(b)

    图 3  卫星在轨动态场景实时匹配方案

    Figure 3.  Real-time matching scheme for satellite dynamic scene

    图 4  不同钳位电压值下的图像直方图

    Figure 4.  Image histograms with different clamping voltage values

    图 5  钳位电压值为500 mV的成像结果

    Figure 5.  Imaging results when clamp voltage value is 500 mV

    图 6  钳位电压设置示意图

    Figure 6.  Demonstration of clamping voltage selection

    图 7  云检测总体思路

    Figure 7.  Overall scheme of cloud detection

    图 8  场景动态范围解算方法

    Figure 8.  Scene dynamic range calculation method

    图 9  无人机平台结构图

    Figure 9.  Structure diagram of UAV platform

    图 10  无人机整机实物图

    Figure 10.  Prototype of UAV

    图 11  不同曝光时间下相同场景的序列图像

    Figure 11.  Sequence images of the same scene with different integration times

    图 12  两次测光图像及动态匹配结果

    Figure 12.  Two photometric images and corresponding dynamic matching results

    图 13  软件界面

    Figure 13.  Simulation software interface

    图 14  模拟测光图像及动态场景实时匹配结果

    Figure 14.  Simulated photometric images and dynamic scene real-time matching results

    表  1  外场实验匹配前后的图像质量评价结果

    Table  1.   Image quality evaluation results of field experiment before and after matching

    图像灰阶 图像熵
    固定曝光 匹配后 提升/% 固定曝光 匹配后 提升/%
    场景1 24.2 204.3 744.2 3.945 6.9490 76.1
    场景2 15.9 153.8 867.3 3.496 6.6881 91.3
    场景3 33.4 217.3 550.6 4.080 6.3607 55.9
    场景4 42.9 232.7 442.4 4.571 7.1191 55.7
    场景5 29.5 189.1 541.0 4.473 6.9889 56.2
    场景6 38.6 209.0 441.5 4.043 6.4393 59.3
    场景7 55.2 229.8 316.3 5.082 7.4558 46.7
    场景8 50.2 182.6 263.7 3.762 6.0114 59.8
    场景9 61.9 226.0 265.1 3.875 5.4261 40.0
    场景10 16.0 157.5 884.4 3.633 6.9047 90.1
    下载: 导出CSV

    表  2  仿真实验图像匹配前后的图像质量评价结果

    Table  2.   Image quality evaluation results of simulation experiment before and after matching

    场景序号 图像灰阶 图像熵 运行时间/ms
    固定曝光 匹配后 提升/% 固定曝光 匹配后 提升/%
    1 96.3 204.3 112.2 5.148 7.117 38.2 234
    2 147 224.1 51.89 5.09 6.22 22.2 281
    3 32.4 251.8 677.1 3.82 6.73 76.1 249
    4 104 234.3 123.7 6.3 7.55 19.8 218
    5 47.3 248.2 424.8 4.88 7.5 53.6 234
    6 55.9 221.4 295.7 4.22 6.43 52.3 234
    7 73.5 209.0 184.4 4.89 6.45 31.9 234
    8 67.6 208.0 207.4 5.09 6.77 33.0 249
    9 64.6 196.3 203.6 5.37 7.18 33.7 266
    10 30.7 209.8 582.3 4.62 7.33 58.6 234
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-12-24
  • 修回日期:  2019-01-18
  • 刊出日期:  2019-06-01

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