Supervised learning for semantic segmentation requires a large number of labeled samples, which is difficult to obtain in the field of remote sensing.
On the one hand, RS-MetaNet raises the level of learning from the sample to the task by organizing training in a meta way, and it learns to learn a metric space that can well classify remote sensing scenes from a series of tasks.
The incorporation of the double self-attention module has an average of 7\% improvement on the pre-class accuracy.
However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information.
The feature-learning procedure of CNN largely depends on the architecture of CNN.