复杂场景下货运源头的高分遥感变化检测与人机协同优化方法OA

To address the issues of low efficiency and insufficient accuracy in freight source supervision under complex scenarios,the paper proposes a high-resolution remote sensing change detection model that integrates human-machine collaborative optimization.By constructing a multi-feature interpretation sign library and performing automated initial screening,combined with a hierarchical human-machine collaboration mechanism for precise target judgment and iterative result optimization.Verification was conducted in a typical area of Beijing.The results show that the model achieved an overall accuracy of 92.7%and a Kappa coefficient of 0.89,improving efficiency by about 60%compared to purely manual interpretation.This model balances automated efficiency with the reliability of human interpretation,providing a precise and efficient technical solution for dynamic supervision of freight sources.