整合超像元分割和峰值密度的高光谱图像聚类

目的 传统图像聚类算法多利用像元的光谱信息，较少考虑图像的空间信息，容易受到噪声干扰。针对该问题，提出一种整合超像元分割（SLIC）和峰值密度（DP）的高光谱图像聚类算法。方法 首先，利用超像元分割技术对高光谱图像进行分割并提取超像元光谱特征；然后，根据提取的超像元光谱特征，计算其峰值密度信息，搜索超像元光谱簇，构建像元与类别间的隶属度关系。最后，利用高光谱模拟数据以及两组真实高光谱图像评价算法的鲁棒性和精度。结果 在不同信噪比的模拟数据中，SLIC-DP算法在调整芮氏指标（ARI）最优的条件下，较K-means和SLIC-Kmeans的方差降低61.86%和41.61%，体现优越的鲁棒性。在高光谱数据集Salinas-A和Indian Pines中，SLIC-DP算法的ARI为0.777 1和0.325 7，较K-Means和SLIC-KMeans聚类算法分别增长10.71%，5.01%与78.86%，25.27%。结论 本文算法抗噪声能力强，充分利用空间信息与光谱信息，有效提升高光谱图像聚类精度。经验证，能满足高光谱图像信息提取和分析的要求，可进一步推广和研究。

Hyperspectral image clustering based on density peaks and superpixel segmentation

Objective 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. Method 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. Result 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. Conclusion 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.