The Fusion of Physical, Textural, and Morphological Information in SAR Imagery for Hurricane Wind Speed Retrieval Based on Deep Learning

This study developed a deep-learning-based model to retrieve sea surface hurricane winds from synthetic aperture radar (SAR) imagery. We introduce the essential idea, residual learning, of the Residual Net into the artificial neural network and design a deep cross-layer concatenation network (DCCN). The model inputs include SAR measured physical parameters in backscattering energy, the texture feature represented by the gray level co- occurrence matrix (GLCM), and the morphological hurricane feature. We collected 45 satellite SAR images from Sentinel-1 (S1) over hurricane conditions. These images were divided into 39 and six for model development and independent testing. A total of 16 127 wind samples acquired from 39 SAR images and simultaneously measured by the stepped frequency microwave radiometer (SFMR) were collected as model tuning datasets, among which 80% and 20% were used for training and validation. Our validation results show that the deep-learning-based model achieved a correlation coefficient (CORR) and root-mean-square error (RMSE) of 0.98 and 1.72 m/s for wind speeds up to 75 m/s. We further applied the model to six independent SAR images. The model significantly outperformed two existing geophysical algorithms and one backpropagation neural network (BPNN) algorithm with the RMSE is 2.61 m/s and a CORR of 0.95. Moreover, statistical analysis in different wind speed regimes indicates that our model shows a stable performance improvement than comparable algorithms. The RMSE decreases 10%–70%, especially the reduction of RMSE is more than 45% at high wind speed (>42 m/s). Furthermore, adding an independent rainfall estimate to the deep-learning model further enhanced the wind retrieval algorithm.