高光谱图像得到了越来越广泛的应用, 但较低的空间分辨率严重地影响着它的应用效果; 其超分辨率方法受到学术界的高度重视, 但一直没有得到很好的解决。 为此重点研究了建立低分辨率资源图像与高分辨率目标图像之间的关系模型; 引入关联感兴趣光谱端元的算子进行空间变换; 应用最大后验概率(MAP)算法实现超分辨率复原。 实验表明, 该超分辨率方法具有超分辨率效果好、 复杂度低、 抗噪声性能强和保护感兴趣类别等优点。

Hyperspectral imagery (HSI) is used in more and more fields, but its low spatial resolution limits its applications severely. The super-resolution algorithm catches more and more eyes but has not been solved well. In this case, the present paper aimed to do the following researches. The relation modeling was constructed between observed HSI of low resolution and target HSI of high resolution. In the modeling, space transformation was implemented by introducing the operator related to endmembers (EMs) of interest. Maximum posterior probability (MAP) algorithm was used to realize the super-resolution (SR) recovery. Experiments show that the proposed SR method has good recovery effect, low computational complexity, robust noise resistance, and can preserve classes of interest.