# An Intelligent Remote Sensing Image Quality Inspection System

Source: [An Intelligent Remote Sensing Image Quality Inspection System](<https://www.sota2.com/research/paper/68fa71291e209131cea4f92d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:59:40.801Z

Authors: Yijiong Yu, Tao Wang, Kang Ran, Chang Li, Hao Wu

Published: 2023-07-22T02:50:07.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2307.11965>)

[PDF](<https://arxiv.org/pdf/2307.11965.pdf>)