Abstract: Hyperion hyperspectral data are frequently contaminated with bad lines, which directly affect the accuracy of the following-up applications. In this paper, a local spectral-spatial similarity measure, or simply LS3M, was proposed, in aids of the Hyperion hyperspectral data characterizing and bad line repairing. Not only the spatial similarity between target pixel and neighborhood similar pixels in the Hyperion hyperspectral image, but the similarity in spectral dimension was considered. That is, LS3M was constituted of spatial similarity measure and spectral similarity measures. The former was measured by Euclidean distance and the latter one combined Canberra distance (CD) and spectral correlation angle (SCA). Considering the radiance characteristics of Hyperion hyperspectral at different bands, information entropy was introduced to constrain the combination of CD and SCA measures with respect to the discrepancies of Hyperion radiance characteristics in different wavelengths. In terms of neighbor search of similar pixels, this paper introduced similarity mean and variance to realize the dynamic setting of the threshold of the spectral combination process. To verify the proposed method, Hyperion hyperspectral data of six typical scenes (i.e., deserts, grasslands, forests, suburbs, coastal cities and inland cities) were utilized to make an experimental study on simulated bad lines and real bad lines in Hyperion image. Besides, a comparison with neighborhood average method, single spectral similarity measures and combined spectral similarity measures was conducted. These results demonstrated that LS3M owned a higher accuracy and better stability on bad line repair, especially in depicting object boundary and topological relations between ground objects.