Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (9): 2541-2546.DOI: 10.11772/j.issn.1001-9081.2019020351

• Artificial intelligence • Previous Articles     Next Articles

Hyperspectral image unmixing algorithm based on spectral distance clustering

LIU Ying1,2, LIANG Nannan1, LI Daxiang1,2, YANG Fanchao3   

  1. 1. College of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an Shaanxi 710121, China;
    2. Key Laboratory of Electronic Information and Application Technology for Scene Investigation, Ministry of Public Security(Xi'an University of Posts & Telecommunications), Xi'an Shaanxi 710121, China;
    3. Key Laboratory of Spectral Imaging Technique, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an Shaanxi 710119, China
  • Received:2019-03-06 Revised:2019-05-13 Online:2019-09-10 Published:2019-05-21
  • Supported by:

    This work is partially supported by the National Natural Science Foundation of China (61571361), the Shaanxi International Cooperation and Exchange Project of China (2017KW-013), the Graduate Innovation Fund for Xi'an University of Posts and Telecommunications (CXJJLY2018024).


刘颖1,2, 梁楠楠1, 李大湘1,2, 杨凡超3   

  1. 1. 西安邮电大学 通信与信息工程学院, 西安 710121;
    2. 电子信息现场勘验应用技术公安部重点实验室(西安邮电大学), 西安 710121;
    3. 中国科学院西安光学精密机械研究所 光谱成像技术重点实验室, 西安 710119
  • 通讯作者: 梁楠楠
  • 作者简介:刘颖(1972-),女,陕西户县人,高级工程师,博士,主要研究方向:图像视频检索;梁楠楠(1994-),女,陕西延安人,硕士研究生,主要研究方向:刑侦图像处理、高光谱图像处理;李大湘(1974-),男,湖南麻阳人,副教授,博士,主要研究方向:数字图像处理、机器学习;杨凡超(1987-),男,陕西西安人,助理研究员,博士,主要研究方向:高光谱成像、偏振技术。
  • 基金资助:



In order to solve the problem of the effect of noise on the unmixing precision and the insufficient utilization of spectral and spatial information in the actual Hyperspectral Unmixing (HU), an improved unmixing algorithm based on spectral distance clustering for group sparse nonnegative matrix factorization was proposed. Firstly, the HYperspectral Signal Identification by Minimum Error (Hysime) algorithm for the large amount of noise existing in the actual hyperspectral image was introduced, and the signal matrix and the noise matrix were estimated by calculating the eigenvalues. Then, a simple clustering algorithm based on spectral distance was proposed and used to merge and cluster the adjacent pixels generated by multiple bands, whose spectral reflectance distances are less than a certain value, to generate the spatial group structure. Finally, sparse non-negative matrix factorization was performed on the basis of the generated group structure. Experimental analysis shows that for both simulated data and actual data, the algorithm produces smaller Root-Mean-Square Error (RMSE) and Spectral Angle Distance (SAD) than traditional algorithms, and can produce better unmixing effect than other advanced algorithms.

Key words: Hyperspectral Unmixing (HU), Hyperspectral signal identification algorithm by minimum error (Hysime), spectral distance metric, Non-negative Matrix Factorization (NMF), remote sensing



关键词: 高光谱解混, 基于最小误差的高光谱信号辨识算法, 光谱距离度量, 非负矩阵分解, 遥感

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