Traditional clustering algorithm usually utilizes more spectrum than spatial information

which is susceptible to noise interference. In this study

we propose a hyperspectral image clustering algorithm based on simple linear iterative clustering (SLIC) and density peaks (DP) to solve the problem mentioned. Based on SLIC

we segment hyperspectral image and extract spectrum in superpixel. According to spectrum characteristics in the extracted superpixel

we calculate DP and search for the superpixel cluster. Clustering is performed by relationship between original pixels and superpixel cluster. The robustness and accuracy of the SLIC-DP algorithm are estimated by simulated hyperspectral data and two sets of real hyperspectral images. SLIC-DP reduces variance (61.86% and 41.61%) compared with K-Means and SLIC-KMeans shows significant robustness. In hyperspectral image of Salinas-A and Indian Pines

Adjust Radom Index (ARI) of SLIC-DP is 0.777 1 and 0.325 7. These rates show 10.71% and 78.86% improvement compared with the K-means algorithm

and 5.01% and 25.27% improvement compared with the SLIC-Kmeans algorithm

which means that SLIC-DP is more accurate than the other algorithms. The SLIC-DP algorithm has strong robustness with better accuracy. The wide use of spectrum and spatial information shows good performance in clustering hyperspectral images.