An Intelligent Remote Sensing Image Quality Inspection System

TL;DR

This paper introduces a deep learning-based system for remote sensing image quality inspection that improves efficiency and accuracy over traditional manual methods by combining classification and segmentation models.

Contribution

It presents a novel two-step system integrating SwinV2 and Segformer models for automated remote sensing image quality assessment, including initial exploration of multimodal models.

Findings

System outperforms traditional quality inspection methods

Achieves high accuracy and efficiency in detecting quality issues

Explores multimodal model applications in remote sensing

Abstract

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.