Object Tracking Using High Resolution Satellite Imagery

High resolution multispectral satellite images with multi-angular look capability have tremendous potential applications. We present an object tracking algorithm that includes moving object estimation, target modeling, and target matching three-step processing. Potentially moving objects are first identified on the time-series images. The target is then modeled by extracting both spectral and spatial features. In the target matching procedure, the Bhattacharyya distance, histogram intersection, and pixel count similarity are combined in a novel regional operator design. Our algorithm has been tested using a set of multi-angular sequence images acquired by the WorldView-2 satellite. The tracking performance is analyzed by the calculation of recall, precision, and F₁ score of the test. In this study, we have demonstrated the capability of object tracking in a complex environment with the help of high resolution multispectral satellite imagery.