Surface-to-Air Missile Sites Detection and Recognition with Large-Scale Remote Sensing Images

As an important part of the air defense and anti-missile system, the surface-to-air missile sites (SAMSs) have important military application value. The existing deep learning-based detection algorithms have the problems of high false alarm rate and low efficiency when applied to large-scale remote sensing images. To address this issue, in this work we propose a multi-task detection and recognition network, including classification branch and detection branch. The classification branch selects the suspected target area from the large-scale remote sensing image, and the detection branch detects and recognizes the target in the suspected area, achieving precise positioning from coarse to fine. In addition, we propose a density clustering algorithm to post-process the detection results, which effectively reduces the false alarm rate of the algorithm. Finally, we propose a SAMS detection and recognition dataset (DSAMS), which divides the SAMSs into three parts: the launch fielding, the launch bunker and the control and guide plain. Comparing our algorithm with other current mainstream target detection algorithms on the DSAMS dataset, our algorithm has significant advantages.