Due to the inevitable presence of quality problems, remote sensing image quality inspection is indeed an indispensable step between the acquisition and the application of remote sensing images. 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 and then accordingly adopts the most appropriate method, such as semantic segmentation, 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.

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