Resumen
Convolutional neural networks have achieved great success in analyzing potential features inside tropical cyclones (TCs) using satellite images for intensity estimation. However, due to the high similarity of visual features in TC images, it is still a challenge to learn the accurate mapping between TC images and numerical intensity. Existing works mainly focus on the visual features of a single TC, ignoring the impact of intensity continuity and time evolution among TCs on decision making. Therefore, we propose a DR-transformer framework for temporal TC intensity estimation. Inside DR-transformers, a novel DR-extractor can extract Distance-consistency(DC) and Rotation-invariance (RI) features between TC images, and therefore can better learn the contours, structures, and other visual features of each TC image. DC features can reduce the estimation error between adjacent intensities, and RI features can eliminate feature deviation caused by shooting angles and TC rotation. Additionally, a transformer with a DR-extractor as the backbone is applied to aggregate the temporal correlation in a series of TC images, which can learn the evolution from intensity to the visual features of TC. Experiments show that the final result, an RMSE of 7.76 knots, outperforms the baseline, and is better than any previously reported method trained on the TCIR dataset.