基于自组织网络的SAR遥感图像的多尺度分割

基于多尺度信息特征和混合模型,将自组织混合网络(SOMN)应用于合成孔径雷达(SAR)图像的分割.首先对SAR图像的多尺度序列进行多尺度随机建模,以此进行多尺度特征提取;然后对其建立混合模型,并经过SOMN进行学习研究得到混合模型的参数;最后再利用Bayesian分类器,对SAR图像进行分割.实验结果表明,本文方法能够充分地利用SAR图像多尺度序列中不同类型地形的统计信息,进而明显地改进了图像的分割质量.

Segmentation for SAR Image Based on Self-organizing Mixture Network

A new method, which combines multiscale technoogy, mixture model and self-organizing mixture network, is proposed for the segmentatioin of SAR image. Firstly,multiscale characteristic is distilled based on the multiscale model of SAR image. Then, the mixture model is found for the multiscale information, and its parameters are estimated by self-organizing mixture network. Lastly,Bayesian classifier is used to segment the SAR image. The experimental results shows the proposed method sufficiently captures the statistical information in a multiscale sequence of SAR image, which is then used to implement the segmentation of SAR image via multiresolution mixture algorithm and self-organizing mixture network, and the perforrnance of segmentation is obviously improved.