Synthetic aperture interferometers formed by satellite formations have been adopted to improve spatial resolution. Due to the limited number of satellites and limited integrated time, the use of sparse baselines can result in distorted reconstructed images, which will generate false targets or miss true targets. When detecting a target on the Earth from a geostationary orbit, the target usually occupies only one pixel, and it is almost submerged by noise. Considering the slow-varying characteristics of the observation area, combined with historical observation data and the motion characteristics of the target itself, a target detection method based on multi-frame snapshot images is proposed. Firstly, the observation background is estimated using multi-frame historical data, and background elimination is used to suppress the background noise. Then, potential targets are selected using the local brightness temperature characteristics of the targets. Lastly, the target motion tracks are applied to erase false targets and correct the positions of missed targets. Simulation experiments have been conducted, and the false alarm rate and the missing alarm rate are counted for randomly distributed targets.