As the volume of marine transportation increases, it becomes increasingly important to monitor ships for efficient coastal monitoring and management. To this end, high-resolution satellite images can be utilized to surveil oceanic environments synoptically. In this study, high-resolution optical satellite image was used to detect ships and estimate the size of each ship in the Korean coastal region. All the pixels in an image were first classified into ship, ship shadow, wake, sea, and land by applying a maximum likelihood classifier. The positions corresponding to the boundary of the ship were obtained from the magnitude of the 2-dimensional gradient on the classified ship pixels, and then the length and width of the ship were estimated by applying an ellipse fitting method to the ship periphery. This method resulted, in slight overestimations of the sizes of the ships. In order to improve the accuracy of the estimated ship sizes, a correction formula was developed by investigating the errors of the estimated values and their potential relationships to the variables representing the spatial shape of the vessels, such as eccentricity, kurtosis. Applying the suggested formulation for ship size estimation improved accuracy by 54.41% compared to the estimated sizes obtained through ellipse fitting. We anticipate that our method of estimating the lengths of the vessels will contribute to identifying missing ships using high-resolution satellite images.