| Abstrakt: |
In recent years, radio frequency interference (RFI) has emerged as a significant challenge in weather radar systems due to the escalating demand on the RF spectrum. This interference results in distortions in radar imagery, diminishing the accuracy of weather forecasts. While current mitigation methods rely on threshold algorithms and linear detection, there's been a notable enhancement with the incorporation of deep learning techniques, especially through convolutional neural networks (CNNs). In this context, we introduce the R2U-Net3+ architecture, a fusion of U-Net3+ and recurrent residual convolutional neural networks (RRCNN). Focusing on detecting RFI-affected areas in radar images, this architecture has exhibited commendable results in tests. Based on evaluation metrics, R2U-Net3+ achieved a dice coefficient of 94.980% with a single recurrence iteration and improved to 95.352% with two iterations. For the IoU, R2U-Net3+ scored 90.745% with one iteration and 91.409% with two iterations. These results affirm that R2U-Net3+ offers a significant improvement in detecting RFI, positioning it as a leading solution to challenges faced by weather radar systems. [ABSTRACT FROM AUTHOR] |
