A Salient Region Detection and Pattern Matching-Based Algorithm for Center Detection of a Partially Covered Tropical Cyclone in a SAR Image

Spaceborne microwave synthetic aperture radar (SAR), with its high spatial resolution, large area coverage, day/night imaging capability, and penetrating cloud capability, has been used as an important tool for tropical cyclone monitoring. The accuracy of locating tropical cyclone centers has a large impact on the accuracy of tropical cyclone track prediction. Usually, the center of a tropical cyclone can be accurately located if the tropical cyclone eye is fully covered by a SAR image. In some cases, due to the limited coverage of the SAR, only a part of a tropical cyclone can be imaged without the eye. From a SAR image processing point of view, these facts make the automatic center location of tropical cyclones a challenging work. This paper addresses the problem by proposing a semiautomatic center location method based on salient region detection and pattern matching. A salient region detection algorithm is proposed, in which the salient region map contains mainly the rain bands of a tropical cyclone in a SAR image. The pattern matching problem is transformed into an optimization problem solved by using the particle swarm optimization algorithm to search the best estimated center of a tropical cyclone. To estimate the accuracy of the located center, we compare the results with the NOAA National Hurricane Center's best track data. Experiments demonstrate that the proposed method achieves good accuracy for locating the centers of tropical cyclones from SAR images that do not contain a distinguishable eye signature.