A Distribution Controllable Simulation Method of Remote Sensing Sea-Ice Images

In the case of sailing out in the sea-ice areas, it is instructive for route planning to research the distribution characters of ice in the target sea. Existing deep learning methods have shown their strength on sea-ice images processing like image classification. Due to the complex environment around sea-ice area, capturing large quantities of images is not easy. Besides, it's often hard to guarantee the abundance of sea-ice distribution of each different scene class, which causes unsatisfactory classification results. Therefore, it is of considerable practical value to research on sea-ice images simulation. In this paper, a distribution controllable simulation method is proposed based on generative adversarial networks for remote sensing sea-ice images. This research can help settle the problem of small sea-ice samples, as well as can provide a practical method for optical image simulation and similar type problems.