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Moving vehicle detection for remote sensing satellite video

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    College of Geoexploration Science and Technology, Jilin University, Changchun 130010, China

  • Email：kangjz@whu.edu.cn
  • Introduction：康金忠，1994年生,男,硕士研究生,研究方向为遥感图像处理与应用。E-mail: kangjz@whu.edu.cn
KANG Jinzhong14,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    Key Laboratory of Earth Observation Hainan Province, Hainan 572000, China

  • Email：wanggz01@radi.ac.cn
  • Introduction：王桂周，1984年生，男，高级工程师，研究方向为遥感大数据智能分析与应用。E-mail: wanggz01@radi.ac.cn
WANG Guizhou23*,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    Key Laboratory of Earth Observation Hainan Province, Hainan 572000, China

HE Guojin23,  
  • Affiliation：

    College of Geoexploration Science and Technology, Jilin University, Changchun 130010, China

WANG Huihui4,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    Key Laboratory of Earth Observation Hainan Province, Hainan 572000, China

YIN Ranyu23,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    Key Laboratory of Earth Observation Hainan Province, Hainan 572000, China

JIANG Wei23,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    Key Laboratory of Earth Observation Hainan Province, Hainan 572000, China

ZHANG Zhaoming23

resumen

With the rapid development of remote sensing satellite imaging technology, remote sensing satellite video provides a new way to acquire moving vehicle target information, and it has become a new data source of vehicle information for intelligent transportation systems. However, in satellite video images, the vehicle is only a few to a dozen pixels and has less contrast with the background. Obtaining the vehicle’s local detail features is difficult. Many problems will arise if the traditional vehicle detection method in monitoring videos is directly applied on satellite videos. Thus, a method that can efficiently exploit and utilize the latest satellite video datasets is urgently needed.On the basis of an analysis of the difference between moving target detection of remote sensing satellite video and traditional monitoring video, a method of moving vehicle detection for remote sensing satellite video automatically constrained by the region of interest was proposed. First, part of the video data is predetected by using the interframe difference method. Then, all the detection results are superimposed together. Morphological processing was perform to obtain the Region Of Interest (ROI) of moving vehicles. Second, moving vehicles were detected based on the improved Gaussian background difference method under the constraint of ROI.Skybox-1 satellite video data were used to qualitatively and quantitatively analyze the accuracy and efficiency of moving vehicle detection. Most of the vehicles were successfully detected and marked out, thereby indicating that the method can be used to detect vehicles in satellite video data. The detection accuracy of our method is more than 93% in all cases, thus indicating that our method has an extremely low false alarm rate. The detection rate is between 70% and 80%, which indicates that the method can accurately detect most of the vehicles in the satellite video data. In addition, the quality of the test is stable at more than 0.84. We can conclude that the method can ensure a high detection accuracy and an optimal detection rate; therefore, the quality of the method is excellent. In this paper, we take the automatic extraction of the moving area as a pretreatment step, which means, after users wait for a few seconds, the program will detect vehicles in the satellite video data set at a near-real-time rate. The method can efficiently exploit and utilize the latest satellite video datasets.The experimental results showed that the proposed method can effectively reduce the number of pseudo-moving targets caused by dynamic background changes, with a high detection rate, high detection quality, very low false alarm rate, and high operating efficiency. Therefore, the detection of moving vehicle targets in a satellite video can be realized effectively.

palabra clave

remote sensing satellite video;Skybox-1;moving vehicles;constraint by region of interest;frame difference;background difference

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