An Unsupervised Particle Swarm Optimization Classifier for SAR Image

Synthetic aperture radar (SAR) image classification is becoming increasingly important in military or scientific research. SAR image classification based on unsupervised learning usually requires optimization of some metrics. Local optimization techniques frequently fail because functions of these metrics with respect to transformation parameters are generally nonconvex and irregular and, therefore, global methods are often required. In this paper, a new evolutionary approach, particle swarm optimization, is adapted for SAR image classification. The new algorithm composes of three main processes: firstly, selecting training samples for every region in the SAR image. Secondly, training these samples using PSO, and obtain clustering center of every region. Finally, output the classification result of SAR image according to clustering center obtained. To show the effectiveness of this approach, experiment with simulated SAR image was considered. The classification results are evaluated by comparing with two well-known algorithms, K-means and fuzzy K-means. According to the overall accuracy and Kappa coefficient, PSO has high classification precision and can be used in SAR images classification