Open AccessHyperspectral Image Unmixing Method Based on Multiple Kernel Graph Non-negative Matrix Factorization
Author(s) -
Jie Yao,
Hongqiao Wang,
Guohong Fu,
Ling Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012149
Subject(s) - hyperspectral imaging , non negative matrix factorization , pattern recognition (psychology) , artificial intelligence , kernel (algebra) , pixel , graph , separable space , computer science , mathematics , feature vector , kernel method , matrix decomposition , computer vision , support vector machine , combinatorics , eigenvalues and eigenvectors , physics , quantum mechanics , mathematical analysis
In order to improve the accuracy of non-negative matrix factorization (NMF) in hyperspectral images unmixing, this paper proposes a hyperspectral image unmixing method based on multiple kernel graph NMF. Using multiple kernel learning (MKL), the hyperspectral data is mapped to the high-dimensional feature space, so that the linearly inseparable hyperspectral data in the low-dimensional space is linearly separable. Introducing the graph regular terms that characterizes the spatial structure relationship between pixels, the internal manifold structure of the hyperspectral image is effectively expressed. The results of both simulated dataset and real dataset show that compared with other method, the proposed method can improve the unmixing accuracy.