Spatiotemporal Data Fusion and CNN Based Ship Tracking Method for Sequential Optical Remote Sensing Images From the Geostationary Satellite

Remote sensing image-based ship detection is an important tool for ocean surveillance. Geostationary orbit (GEO) satellite is characteristic with a high temporal resolution, which makes continuous monitoring possible. However, the ships in such images are generally quite tiny. Traditional methods usually detect tiny ships with the character of shape, lightness and contrast, which are not resistant to false alarms from fractus. In this letter, a network-based detection method is adopted to extract spatio-temporal jointly features to distinguish the real ships from other distractions, especially in fractus concentered scenes. Based on detection results, an intersection over union (IoU) based target matching method is proposed to form the trails, suppressing the false alarms at the same time. To reduce the false alarm further, a structure similarity (SSIM) based appearance consistency measurement is utilized to remove objects whose appearance change over time. The experiment results show that the proposed method detects and tracks ships with high recall and low false alarm, compared with the traditional tracking methods. It could be applicated in GEO remote sensing images based ocean surveillance in various kinds of scenes.