结合产生式模型和RCC方法的极化SAR图像分类算法

An improved algorithm of SAR image classification based on generative model and RCC

摘要:

为了充分利用图像中的上下文信息对空间关系进行推理,提出了一种基于产生式模型和区域连接演算(Region Connection Calculus,RCC)的新模型——GM-RCC模型(Generative Model based on RCC),用于合成孔径雷达(SAR)图像的分类研究．首先,通过建立图像金字塔将一幅SAR图像过分割成多尺度的超像素,然后利用层次RCC模型对这些超像素的空间关系进行描述,其中RCC关系的学习和推理都是在产生式模型的框架下进行的．在模型的推理过程中采用了迭代策略以获得更加精细的分类结果．实验选用了极化特征及其他典型特征,并在SAR图像集上进行了实验,实验结果证明了该算法的有效性．

Abstract:

To take full use of context information to learn the spatial relationship of the image, a novel model based on Generative Model and Region Connection Calculus (RCC) is proposed in this paper. We name it GM-RCC model, which is used for SAR image classification. Firstly, a SAR image is over-segmented into multi-scale super pixels via adopting the image pyramid. Then the hierarchical RCC model is utilized to describe the spatial relationships among these super pixels. All hierarchical RCC relationships are learned and reasoned under the Generative Model reasoning framework. The experiments are carried out on SAR image datasets and polarimetric features are selected with other typical features. The results reveal the efficient performances and superiorities of the proposed algorithm.