Quickbird遥感影像的车辆自动检测与运动参数估计

张博研; 李广泽; 武星星

doi:10.3788/YJYXS20153004.0687

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Quickbird遥感影像的车辆自动检测与运动参数估计

成像技术与图像处理 | 更新时间：2020-08-12

- Quickbird遥感影像的车辆自动检测与运动参数估计
- Speed estimation and automatic detection of moving vehicle from Quickbird satellite images
- 液晶与显示 2015年30卷第4期页码：687-694
- 作者机构：
  
  中国科学院长春光学精密机械与物理研究所,吉林长春,130033
- 作者简介：
  
  [ "张博研(1987-),女,吉林白城人,硕士,研究实习员,主要从事立体测绘和图像处理方向的研究。E-mail:boyan1021@163.com" ]
  [ "李广泽(1976-),男,吉林长春人,博士,研究员,主要从事空间遥感相机嵌入式控制系统设计与图像处理方向的研究。E-mail:Lgzandrew@tom.com" ]
  [ "武星星(1980-),男,河南洛阳人,博士,副研究员,主要从事空间光电技术及智能控制方向的研究。E-mail:starglare@126.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(No.61108066)();吉林省科技发展计划项目(No.20130101028jc)()
- DOI：10.3788/YJYXS20153004.0687
  中图分类号： TP79
- 收稿日期：2014-10-21，
  
  修回日期：2014-11-15，
  
  纸质出版日期：2015-08-05
- 稿件说明：
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张博研, 李广泽, 武星星. Quickbird遥感影像的车辆自动检测与运动参数估计[J]. 液晶与显示, 2015,30(4): 687-694

ZHANG Bo-yan, LI Guang-ze, WU Xing-xing. Speed estimation and automatic detection of moving vehicle from Quickbird satellite images[J]. , 2015,30(4): 687-694
张博研, 李广泽, 武星星. Quickbird遥感影像的车辆自动检测与运动参数估计[J]. 液晶与显示, 2015,30(4): 687-694 DOI： 10.3788/YJYXS20153004.0687.

ZHANG Bo-yan, LI Guang-ze, WU Xing-xing. Speed estimation and automatic detection of moving vehicle from Quickbird satellite images[J]. , 2015,30(4): 687-694 DOI： 10.3788/YJYXS20153004.0687.

摘要

遥感图像的车辆目标提取与运动参数估计在交通管理、战场态势分析等领域具有广阔的应用前景

但目前相关算法均需要人工参与或借助GIS信息

针对上述问题提出了一种基于计算机视觉的全自动车辆检测与运动参数估计算法。分析了Quickbird全色与多光谱传感器的焦平面结构特征以及该结构造成的鬼影现象;针对全色与多光谱遥感影像的分辨率高、光谱信息丰富的特点

利用植被指数归一化、图像分割、形态学灰度重建等图像处理过程

实现了全色图像中运动车辆的自动检测

在此基础上检测低分辨率的多光谱图像中的目标。利用全色与多光谱图像的成像时间差估计运动参数。在Quickbird遥感影像的验证实验中充分证明了算法的可行性与正确性。

Abstract

Velocity estimation and vehicle detection from satellite images is widely used in the domain of traffic monitoring

battlefield analysis etc.But most of the algorithms in this area are either manual or incorporating ancillary data.A new method has been developed to extract vehicles automatically and determine their speeds based on computer vision.First of all

the Ghost phenomenon of moving targets is analyzed by considering the structure of Quickbird panchromatic and multispectral focal plane.Normalized differential vegetation index

image segmentation and morphological gray scale reconstruction are combined to realize accurate vehicles detection in panchromatic image; subsequently image matching is applied to extract vehicles in the multispectral image based on the vehicle positions in panchromatic image.The speed can be calculated by using the vehicle extraction results and time intervals between panchromatic and multispectral images.Finally

this approach was tested on several images of Quickbird covering different backgrounds and can obtain a detection rate as high as 90%.The performance shows that the algorithm is practicable and valid.

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Keywords

references

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