In this paper, we propose a novel method for efficient boundary detection in close-range hyperspectral images (HSI). We adopt different spectral similarity measurements to construct a sparse spectral-spatial affinity matrix that characterizes the similarity between the spectral responses of neighboring pixels within a local neighborhood. After that, we adopt a spectral clustering method in which the eigenproblem is solved and the eigenvectors of smallest eigenvalues are calculated. Morphological erosion is then applied on each eigenvector to detect the boundary. We fuse the results of all eigenvectors to obtain the final boundary map. Our method is evaluated on a real-world HSI dataset and compared with three alternative methods. The results exhibit that our method outperforms the alternatives, and can cope with several scenarios that methods based on color images can not handle.