Small moving vehicle detection via local enhancement fusion for satellite video

Abstract

ABSTRACT Satellite video is an emerging data source for dynamic Earth observation, which provides us with a new means for large-scale moving vehicle detection and traffic monitoring. However, the ratio of the foreground to background in satellite video is severely uneven. Furthermore, due to the high altitude and spatial resolution of video satellites, the moving vehicles often have a small size and low contrast with the background. There is also complex environment and background movement due to the imaging mode of satellite video, which makes the vehicle detection more challenging. In this paper, to solve the problems of the low contrast and the dynamic background in satellite video, a local enhancement fusion Gaussian mixture model and improved three-frame difference detection framework, namely E-GMMTFD, is proposed to detect small moving vehicles in satellite video. In the E-GMMTFD framework, the local contrast enhancement method is used to improve the contrast between the target and the background. The Gaussian mixture model (GMM) and the three-frame difference (TFD) method are fused by a logical operation to detect moving vehicles. The E-GMMTFD framework utilizes the adaptability to complex environment changes of the GMM and the strength of the TFD method to eliminate background movement, thus reducing the false alarms caused by illumination change and background movement. Three experiments were performed on moving vehicle detection using Jilin-1 satellite video and SkySat-1 satellite video. The experimental results confirmed that the proposed framework can be efficiently used to detect moving vehicles in the large scale of satellite video. Based on the vehicle detection results for the satellite video, the traffic density of the urban road network can provide technical support for alleviating urban traffic congestion.