Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4633120 | Applied Mathematics and Computation | 2008 | 6 Pages |
Abstract
This paper proposes an effective multiscale method for the segmentation of the synthetic aperture radar (SAR) images via probabilistic neural network. By combining the probabilistic neural network (PNN) with the multiscale autoregressive (MAR) model, a classifier, which inherits the excellent strongpoint from both of them, is designed. The MAR models are utilized to extract the multiscale feature of SAR image, which is the input of the network. The PNN is trained by the proposed algorithm, and then the SAR images are segmented by the trained network. The experimental result demonstrates the effectiveness and efficiency of the proposed method.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Jin-Juan Quan, Xian-Bin Wen, Xue-Quan Xu,