An Attention-Aided Convolutional Neural Network for Global Sea Surface Wind Speed Estimation from Gnss-R Data

Global navigation satellite system reflectometry (GNSS-R) is an emerging remote sensing technology for sea surface wind speed measurement. GNSS-R captures the reflected signal from sea surface and generates Delay Doppler maps (DDM). Existing studies have demonstrated the effectiveness of convolutional neural network (CNN) in retrieving wind speed from GNSS-R. However, most of the existing CNN-based methods assign equal importance to each pixel in the DDM and cannot focus on more discriminative pixels. To address this issue, in this study, the attention mechanism is integrated and an attention-aided CNN (Att-CNN) is developed for sea surface wind speed estimation. The performance of Att-CNN is evaluated on the Cyclone GNSS (CYGNSS) data. Compared to conventional CNN without attention module, the proposed Att-CNN obtains a lower RMSD of 1.151 m/s with an improvement of 3.46%.