Segmentation of SAR Images Based on the Optimal Level Sets Using CWOA
Shiyu Luo, Ling Tong · 2020
In the previous work, the presented multi-texture-based segmentation model based on level sets for synthetic aperture radar (SAR) images generally can attain good results. However, the weighting parameters in this model have non-ignorable influences on segmentation performance, while such parameters usually are determined by the past experiences, which decreases the flexibility of this method. To address this problem, based on this model, we propose a SAR image segmentation method with the optimal level sets using chaotic whale optimization algorithm (CWOA). First, various image segmentation results are attained by the multi-texture-based model with several random sets of weighting parameters, samples are then automatically generated by comparing these segmentation results. Second, search agents (humpback whales) are defined with respect to the weighting parameters that need to be optimized, and the fitness function (prey) is associated with the samples. The optimal level sets are then established by integrating the multi-texture-based model and CWOA. Finally, the experimental result achieved from a SAR image shows the effectiveness of the proposed method.