An Insect and Bird Echoes Classification Method Based on Point-Surface Features Using X-Band Weather Radar

Animal migration poses risks to human health and economic stability, highlighting the need for effective monitoring. Weather radars are essential tools for monitoring migratory insects and birds. While S-band radars can accurately distinguish insect and bird echoes, X-band radar, offering higher resolution, has not been sufficiently explored, limiting its use in aerial ecological monitoring. In this article, joint observational experiments were conducted to evaluate insect and bird echoes from S-band and X-band weather radars. The results show significant overlap in the polarization features on X-band radar, making existing algorithms unsuitable for X-band data. To address this issue, a point-surface feature fusion method is proposed. This approach extracts polarization variables to construct point-scale features for initial classification with statistical models. A residual network captures surface-scale morphological features, which are integrated with the point-scale recognition results. Finally, a feature fusion module generates the final classification. The method achieves a mean intersection over union (mIoU) of 84.56% and demonstrates high accuracy and robustness in historical data tests. This study enhances X-band radar’s ability to differentiate between insect and bird echoes, providing a new solution for aerial ecological monitoring.