A new PCNN-based method for segmentation of SAR images

This study describes a new method for segmentation of Synthetic Aperture Radar (SAR) images, which integrates optimal threshold with pulse-coupled neural network (PCNN). Traditional image segmentation algorithms exhibit weak performance for SAR images due to the poor quality of SAR images. PCNN has been widely used in image segmentation. However, satisfactory results are usually obtained at the expense of time-consuming selection of PCNN parameters and the number of iterations. Simplified unit-linking PCNN with only one parameter to be determined are used in the proposed method. The method initiates segmentation with the optimal threshold so one iteration is needed. The method demonstrates accuracy and fast performance in segmentation results and in processing speed compared to those PCNN segmentation algorithms which requires determining the number of iterations and image entropy. Moreover, the method is not sensitive to noise and intensity. Experimental results show the effectiveness of the proposed method. This method aims to be possible in real-time hardware implementation.