  Due to the inevitable presence of quality problems, quality inspection of
remote sensing images is indeed an indispensable step between the acquisition
and the application of them. However, traditional manual inspection suffers
from low efficiency. Hence, we propose a novel deep learning-based two-step
intelligent system consisting of multiple advanced computer vision models,
which first performs image classification by SwinV2 and then accordingly adopts
the most appropriate method, such as semantic segmentation by Segformer, to
localize the quality problems. Results demonstrate that the proposed method
exhibits excellent performance and efficiency, surpassing traditional methods.
Furthermore, we conduct an initial exploration of applying multimodal models to
remote sensing image quality inspection.
