We believe that a general model which is trained by a label-free and task-independent way may be the next paradigm for RSIU and hope the insights distilled from this study can help to foster the development of an original vision model for RSIU.
In this paper, a robust matching method based on the Steerable filters is proposed consisting of two critical steps.
Supervised learning for semantic segmentation requires a large number of labeled samples, which is difficult to obtain in the field of remote sensing.
In this paper, we propose to exploit the underlying structures of the state-action value function, i. e., Q function, for both planning and deep RL.
We show that this process destroys the adversarial structure of the noise, while re-enforcing the global structure in the original image.