This paper proposes a Gaussian process-based visual servoing framework that not only overcome the weakness of the standard image-based visual servoing (IBVS) scheme but also improve the vision-enabled and real-time control performance. In particular, the proposed framework provides the Gaussian process-based sampled image path consisting of a set of the references between the initial and desired positions of a stationary or moving target in the image plane with respect to the height under the large translation and rotation error in order to overcome the weakness of the standard IBVS scheme. Furthermore, we applied the proposed framework to the aerial parallel manipulator during the picking-n-replacing mission. The developed vehicle has two vision system: one is the gimbal-stabilized pinhole camera on the host vehicle and the other is the fisheye camera with the one-dimensional light detection and ranging (LiDAR) fixed on the end-effector of the parallel manipulator. The proposed framework can generate the feasible control input according to each sensor system`s features. In this paper, the preliminary results and analysis are represented. The results of simulations and flight tests will be conducted to verify the performance of the proposed framework indicate that it can return the path points for the convergence of the desired position while the target is moving or stationary, even with the large scale difference.