Abstract
In recent years, satellites capable of capturing videos have been developed and launched to provide high definition satellite videos that enable applications far beyond the capabilities of remotely sensed imagery. Moving object detection and moving object tracking are among the most essential and challenging tasks, but existing studies have mainly focused on vehicles. To accurately detect and then track more complex moving objects, specifically airplanes, we need to address the challenges posed by the new data. First, slow-moving airplanes may cause foreground aperture problem during detection. Second, various disturbances, especially parallax motion, may cause false detection. Third, airplanes may perform complex motions, which requires a rotation-invariant and scale-invariant tracking algorithm. To tackle these difficulties, we first develop an Improved Gaussian-based Background Subtractor (IPGBBS) algorithm for moving airplane detection. This algorithm adopts a novel strategy for background and foreground adaptation, which can effectively deal with the foreground aperture problem. Then, the detected moving airplanes are tracked by a Primary Scale Invariant Feature Transform (P-SIFT) keypoint matching algorithm. The P-SIFT keypoint of an airplane exhibits high distinctiveness and repeatability. More importantly, it provides a highly rotation-invariant and scale-invariant feature vector that can be used in the matching process to determine the new locations of the airplane in the frame sequence. The method was tested on a satellite video with eight moving airplanes. Compared with state-of-the-art algorithms, our IPGBBS algorithm achieved the best detection accuracy with the highest F1 score of 0.94 and also demonstrated its superiority on parallax motion suppression. The P-SIFT keypoint matching algorithm could successfully track seven out of the eight airplanes. Based on the tracking results, movement trajectories of the airplanes and their dynamic properties were also estimated.
Highlights
The advent of video satellites opens a new chapter in Earth observation [1,2,3,4,5,6,7]
The performance of the Improved Gaussian-based Background Subtractor (IPGBBS) algorithm was compared with three state-of-the-art algorithms: Codebook [15], Mixture of Gaussians (MoG) [14], and Visual Background Extractor (ViBe) [16,17]
These algorithms have been widely used in computer vision for detecting moving objects in ordinary videos, but only ViBe has been applied to satellite videos for moving vehicle detection [4,5]
Summary
The advent of video satellites opens a new chapter in Earth observation [1,2,3,4,5,6,7]. The detection of foreground pixels in each video frame is often achieved by subtracting background pixels [12]. The simplest background model is the average or median of multiple video frames [2,7]. Chris and Grimson [14] proposed a more powerful Mixture of Gaussian (MoG) model to distinguish real foreground from moving background. This model, may produce incorrect detection if intensity changes
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