A Multi-Feature Fusion-Based Method for Crater Extraction of Airport Runways in Remote-Sensing Images

Due to the influence of the complex background of airports and damaged areas of the runway, the existing runway extraction methods do not perform well. Furthermore, the accurate crater extraction of airport runways plays a vital role in the military fields, but there are few related studies on this topic. To solve these problems, this paper proposes an effective method for the crater extraction of runways, which mainly consists of two stages: airport runway extraction and runway crater extraction. For the previous stage, we first apply corner detection and screening strategies to runway extraction based on multiple features of the runway, such as high brightness, regional texture similarity, and shape of the runway to improve the completeness of runway extraction. In addition, the proposed method can automatically realize the complete extraction of runways with different degrees of damage. For the latter stage, the craters of the runway can be extracted by calculating the edge gradient amplitude and grayscale distribution standard deviation of the candidate areas within the runway extraction results. In four typical remote-sensing images and four post-damage remote-sensing images, the average integrity of the runway extraction reaches more than 90%. The comparative experiment results show that the extraction effect and running speed of our method are both better than those of state-of-the-art methods. In addition, the final experimental results of crater extraction show that the proposed method can effectively extract craters of airport runways, and the extraction precision and recall both reach more than 80%. Overall, our research is of great significance to the damage assessment of airport runways based on remote-sensing images in the military fields.