Applying Particle Swarm Intelligence in PolSAR Image Clustering

Abstract

The clustering problem of polarimetric SAR image is an optimization problem with high dimension and large amount of data. Aiming at the problem that the classical unsupervised classification methods for High Resolution Polarimetric SAR images are difficult to find the global optimal solution. The Particle Swarm Optimization (PSO) algorithm was proposed in High Resolution PolSAR images clustering. For the first beginning, the scattering eigenvalues of PolSAR data were used for initial classification, and then followed by the computation of clustering center and initialization of PSO algorithm, finally the particle swarm are introduced in the iterative steps to reduce noise effect and improve the classification results. The performance of this novel method is demonstrated in experiments using L-Band PolSAR image of San Francisco Bay.

Forest canopy height estimation using particle swarm optimization and TerraSAR data

“…By integrating equations ( 1) and ( 2), the objective function could be designed for the particles which is called "fitness". And the solution element which is named "position" in the feature space was combined with the coherence coefficient γv, extinction coefficient σ and the forest height hv 11 . The flow of the PSO-improved forest height inversion method is as follows:…”