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The precise location of the tropical cyclone (TC) center is critical for intensity estimation and trajectory prediction. Due to the variability of TC morphology and structure, there are still some challenges in locating its center automatically. The ability of the deep convolutional network to capture multilevel structural features of the images is exploited. Furthermore, a two-step scheme for locating the TC center is proposed, which contains the object detection for TCs with deep learning and the comprehensive decision for TC centers. In the object detection, considering the statistical scale distribution of TCs, the global and local features extracted by the network are combined to form the fusion feature maps through the upsampling and concatenation. The changes in the TC scale are accommodated by two different scale outputs. A high detection rate and a low false alarm rate are obtained with the object detection, which provides an initial position for the TC center. Within the scope of the TCs, the final position of the center is obtained through segmentation, edge detection, circle fitting, and comprehensive decision. The experimental results show that the average latitude and longitude error of the proposed method is about 0.237$^\circ$. For the TC in the initial phase or dissipation stage, the location results are usually superior to the results of the comparison algorithms.

A Center Location Algorithm for Tropical Cyclone in Satellite Infrared Images