haze always exists in hyper-spectral remote sensing imagery, and it is a key reason that influences the effective information extraction of hyper-spectral images. specially, when the faint haze covers part of the target in remote sensing images, the target still can be detected but not clear. so, how to remove the influence of the haze and improve the applicable efficiency of hyper-spectral images is a popular research point. this paper proposes a dehazing method for hyper-spectral images based on linear unmixing. first, a popular hyper-spectral unmixing method called fun is used to get the signature of all the end-members and their corresponding abundance. and then, the abundance of the haze end-member is removed and the abundances of the rest end-members are adjusted to satisfy the sum-to-one and non-negative constraint. lastly, the new abundance and the signature of the end-members are linearly mixed to get the dehazed hyper-spectral images. the experiment result shows that the dehazed hyper-spectral images exhibit better target information and details. the method is effective and available. © 2016 spie.