中文摘要：

为充分利用高光谱遥感影像中丰富的光谱和空间信息,提出了一种基于多核支持向量机（multiple kernel support vector machine,MKSVM）和马尔科夫随机场（markov random field,MRF）的影像分类方法。该方法首先利用MKSVM分类器对影像进行分类处理,再利用MRF对初始分类结果进行空间结构规则化,得到最终分类结果。通过对AVIRIS高光谱影像的分类实验表明,该方法有效地消除了分类结果中同质区域内的“噪声”,分类精度提高了3%左右。

英文摘要：

To fully utilize the spectral and spatial information rich in hyperspectral remote sensing images, this paper proposes a hyperspectral images classification method based on multiple kernel support vector machine （MKSVM） and Markov random field （MRF）. Firstly, the MKSVM classifier is used to classify hyperspectral images, then the MRF is used to regularize the initial classification results in the spatial structure, and the final classification results are obtained in the end. The experiment on AVIRIS hyperspectral image shows that the proposed method not only effectively eliminates the ＂noise＂ in the homogeneous regions within the classification results but also improves the classification accuracy by about 3 %.