Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1548504 | Progress in Natural Science: Materials International | 2009 | 4 Pages |
Abstract
A novel method that hybridizes genetic algorithm (GA) and expectation maximization (EM) algorithm for the classification of synthetic aperture radar (SAR) imagery is proposed by the finite Gaussian mixtures model (GMM) and multiscale autoregressive (MAR) model. This algorithm is capable of improving the global optimality and consistency of the classification performance. The experiments on the SAR images show that the proposed algorithm outperforms the standard EM method significantly in classification accuracy.
Related Topics
Physical Sciences and Engineering
Materials Science
Electronic, Optical and Magnetic Materials
Authors
Xianbin Wen, Hua Zhang, Jianguang Zhang, Xu Jiao, Lei Wang,