Superpixel-Based Weak Biological Feature Echo Extraction Method for Weather Radar

Accurately extracting biological echoes is a fundamental prerequisite for weather radar aeroecology monitoring. However, the concurrent presence of meteorological echoes and biological echoes greatly restricts the extraction accuracy. Traditional neural network-based echo extraction algorithms rely on the spatial continuity feature of the echoes. But, the concurrent presence of multiple types of echoes will lead to the invalidation of the spatial feature and the error of boundary identification of biological echoes. To address this challenge, this study proposes a weak biological echo extraction algorithm using a superpixel technique, aimed at preserving richer biological details in adverse weather conditions. To amplify the imaging distinctions between biological and meteorological components, we design 8-D differential features for each superpixel patch on the CIELAB color space. The gradient boosting tree model is trained for biology classification in handling complex data scenarios. Trained trees exhibit strong generalization capabilities and imbalanced testing data that reflect real weather conditions. To mitigate the limitations posed by the lack of publicly available datasets, we establish a trainable weather radar image dataset encompassing typical weather conditions across national weather radar stations. Experimental results validated that the algorithm retains over 98% of biological data under adverse weather conditions.