Unsupervised Polarimetric SAR Image Classification using Gp0 Mixture Model

JUAN IGNACIO FERNÁNDEZ MICHELLI; MARTÍN HURTADO; JAVIER ARETA; CARLOS MURAVCHIK

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Lugar: New York; Año: 2017 vol. 14 p. 754 - 758

1545-598X

 This letter proposes a polarimetric Synthetic Aperture Radar (SAR) image classification method based on the Expectation-Maximization (EM) algorithm. It is an unsupervised algorithm that determines the number of classes in the scene following a top-down strategy using a covariance based hipothesis test. A Gp0 mixture model is used to describe Multilook Complex (MLC) polarimetric data, and the proposed algorithm is tested in simulated and real datasets obtaining good results. The classification performance is evaluated by means of the overall accuracy and the kappa indices obtained from Montecarlo analysis. Finally, the results are compared to those obtained by other classic and recently developed classification algorithms.