Abstract:

In order to accurately identify meteorological targets and non-meteorological targets mixed in the meteorological targets, a meteorological target recognition method that combines light gradient boosting machine (LightGBM) and residual network of residual network: next generation(ResNeXt) network is proposed. Firstly, a block sample dataset is produced, and this dataset is used as a driver to establish a meteorological target recognition network model based on the ResNeXt. This model realizes a coarse-grained recognition of meteorological targets with block data samples as the recognition unit with the recognition accuracy of more than 99.6%. Then, the difference between the coarse-grained recognition results and the reference data is included in the LightGBM classifier so the fine-grained recognition results with the radar sampling unit as recognition unit are obtained. Combined with the actual observation data, it is proved that the proposed method combines the ability of fine-grained recognition of LightGBM with the ability of high-precision recognition of ResNeXt, which can distinguish meteorological targets from clutter, and the distinguishing results are highly consistent with the reference results.

Key words: eteorological radar, meteorological target recognition, residual network, light gradient boosting machine (LightGBM), fusion, deep learning