随着遥感卫星成像技术的快速发展，遥感视频卫星的出现为运动车辆目标信息获取提供了新手段。然而在遥感卫星视频中，运动车辆目标仅为几个或十几个像素并与背景的对比性较低，难以获取车辆的局部细节特征，使得传统监控视频的运动车辆检测方法直接应用到卫星视频图像会存在很多的问题。在分析遥感卫星视频运动车辆目标检测与传统监控视频运动车辆目标检测差异的基础上，本文提出了一种感兴趣区域自动约束的遥感卫星视频运动车辆快速检测方法：首先是快速自动获取运动车辆目标的感兴趣区域；其次在感兴趣区域约束下，基于改进的高斯背景差分方法实现感兴趣区域内运动车辆快速检测。应用Skybox-1卫星视频数据进行了运动车辆目标检测实验并进行了定性与定量分析。实验结果表明，本文方法可以有效自动减少动态背景变化导致的伪运动目标，具有较高的检测率、较高的检测质量、极低的虚警率以及较高的运行效率，可自动高效实现卫星视频图像中运动车辆目标的高精度检测。

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.