中文摘要：

针对基于像素或基于区域的马尔科夫随机场（Markov random field,MRF）模型仅能描述单一层次影像数据特性的局限,提出了一种综合像素和区域特性的多层次MRF模型,以提高MRF模型表达遥感数据层次特性的能力。为利用高分辨率遥感影像几何结构信息来提高不同地物的可区分性,提出了一种描述地物结构特性的形状特征,用于区分光谱特性相似的不同地物。本文的分类算法包括两个过程：首先,基于像素和区域特征,采用多层次MRF模型进行初始分类;然后,基于形状特征采用SVM对第一步分类结果中易混淆的地物进行分类。根据不同地物采用合适特征量描述可在特征空间中增加可区分性的事实,采用形状特征对基于层次MRF模型的错分类别进行再分类可有效改善分类精度。同现有基于单一层次的方法相比,实验结果表明该算法的分类性能有了明显的改善。

英文摘要：

In order to unitize the image information at different level, this paper introduces a novel approach to integrate the pixel and region feature into MRF model. A structure feature descriptor is proposed to represent the structural characteristics of objects to disambiguate land cover types with similar spectral characteristics. The first step of the proposed algorithm is to classify the input image by using the multi-level MRF model, then the structural feature is used to classify the land cover types prone to misclassified based on the result of the first step. The proposed algorithm is evaluated by being compared with the result with single level MRF model and other existing classification method. Qualitative and quantitative experimental results show that the proposed algorithm can effectively capture the image data characteristics at different level which result in higher classification accuracy.