Tropical Cyclone Cloud Image Segmentation by the B-Spline Histogram with Multi-Scale Transforms

An e±cient tropical cyclone (TC) cloud image segmentation method is proposed by combining the curvelet transform, the cubic B-Spline curve, and the continuous wavelet transform. In order to enhance the global and local contrast of the original TC cloud image, a second-generation discrete curvelet transform is implemented for the original TC cloud image. Based on our prior work, the low frequency components are enhanced by using an incomplete Beta transform and the genetic algorithm in the curvelet domain.Then the enhanced TC cloud image is used to segment the main body of the TC from the TC cloud image.First, pre-processing is implemented by B-Spline curves to the original TC cloud image to remove unrelated small cloud masses. A region of interest (ROI) which includes the main body of TC can thus be obtained.Second, the gray-level histogram of ROI is obtained. In order to reduce oscillations of the histogram, the gray-level histogram is smoothed by cubic B-Spline curves and the B-Spline histogram is obtained. The one dimensional continuous wavelet transform is employed for the curvature curve of the B-Spline histogram.A new segmentation cost criterion is given by combining threshold, error, and structure similarity. The optimally segmented image can be obtained by the criterion in the continuous wavelet domain. The optimally segmented image is post-processed to obtain the final segmented TC image. The experimental results show that the main body of TC can be effectively segmented from the complex background in the TC cloud image by the proposed algorithm.