Abstract: To improve the detection quality of moving vehicles from video satellites,study was carried out from two dimensions: increasing the number of correct detection of moving objects and suppressing the number of false detection of moving objects.A new moving vehicle detection method was proposed based on the visual background extractor (ViBE) algorithm and the object-oriented classification technology in remote sensing field.By optimizing the parameters of the ViBE,real moving objects can be obtained as much as possible,but this introduced a lot of false objects to some extent.Therefore,according to the interdependent relationship between moving vehicles and road,objectoriented classification method was adopted to extract road and further filter the false objects produced by ViBE.A total of eight features including the mean value and standard deviation of spectral attribute,average gray value and entropy in the convolution kernel of texture attribute,area,length,tightness and extension of spatial geometric attributes were used.The results show that, compared with the three frames difference method and ViBE detection method,the moving vehicles detection accuracy of the proposed method has been significantly improved.The detection precision P of three frames difference method,ViBE and VOMVD for moving objects are 70.91%,61.49% and 85.71% respectively,the recall R is 84.78%,98.91% and 97.83% respectively,and the F score is 77.23%,75.83% and 91.37% respectively.The effect of the proposed method on moving vehicles detection has been improved effectively.

Key words: satellite video, moving object detection, ViBE, object-oriented classification, remote sensing road extraction