An Intelligent Remote Sensing Image Quality Inspection System
This addresses quality inspection inefficiencies for remote sensing applications, but it appears incremental as it combines existing models without major breakthroughs.
The paper tackles the problem of inefficient manual quality inspection of remote sensing images by proposing a deep learning-based two-step intelligent system that uses SwinV2 for classification and Segformer for segmentation, resulting in excellent performance and efficiency surpassing traditional methods.
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.