为了融合多光谱图像空间信息和高光谱图像光谱信息，进而提高高光谱图像的空间分辨率，提出了一种基于相关向量机(RVM)的高光谱图像超分辨率算法。介绍了多光谱与高光谱图像通过融合获得超分辨率图像的算法原理，对RVM回归原理进行分析介绍。结合RVM在回归分析上的优势，提出了利用RVM建立多光谱图像与高光谱图像之间的内在的空间及光谱对应关系，通过融合两种图像的信息来提高图像的分辨率。实验结果表明：归一化均方根误差小于0.001,光谱角误差小于0.02,较Price、Elbakary法有较大提升。本文提出的方法对高光谱图像重建具有良好的效果，可为分类、目标检测和识别提供更合适的数据源。

In order to improve the space resolution of hyper-spectral image by fusing the spatial information of multispectral images and the spectral information of hyperspectral images, a hyperspectral image super-resolution algorithm based on relevance vector machine (RVM) is proposed. A brief introduction of the principle of the Price method which fuses multispectral and hyperspectral images to get the super-resolution image is given, and the RVM linear regression is introduced. Combining with the advantages of RVM in regression analysis, a resolution enhancement by revealing the corrspondence of the spatial and spectral information is gotten. The experiment results show that the normalized root-mean-square (RMS) is lower than 0.001 and the spectral angel error is lower than 0.02, which gets a great improvement compared with the results of the Price method and the Elbakary method. The method proposed has a significant result in hyperspectral image reconstruction, which provides a much properer data source for classification, object detection and recognition.