Detection and Localization of Projectile Impact Point Based on Remote Sensing Imagery

In order to improve the accuracy of localization of projectile impact point, detection and localization methods of projectile impact point based on remote sensing imagery are studied in this paper. We constructed a labeled dataset named SFP about smoke, fire, and impact point containing 6700 images. We designed a two-stage segmentation algorithm based on prior knowledge. Our algorithm first segments the region containing smoke and fire, which is treated as the Region of Interest (ROI). Within the ROI, our algorithm then segments impact point. Our algorithm is compared with the direct segmentation algorithm on SFP. The precision, recall, and mAP of our algorithm are 0.693, 0.654, and 0.639. And those of the direct segmentation algorithm are 0.263, 0.175, and 0.041. The performance of our algorithm is much higher, which indicates that our algorithm can accomplish the task of detecting and localizing the impact points based on remote sensing imagery better.