Application of spatially related MRF model in NMF hyperspectral unmixing
Aiming at Non-negative Matrix Factorization(NMF)s problem of initialization and local minima in hyperspectral unmixing,a NMF linear unmixing algorithm with spatial correlation constrains(SCNMF)based on Markov Random Field(MRF)was proposed.Firstly,Hyperspectral Signal identification by minimum error(HySime)method was adopted to estimate the number of endmembers,initialized endmember matrix and abundance matrix by Vertex Component Analysis(VCA)and Fully Constrained Least Squares(FCLS)respectively.then established energy function to depict the spatial distribution characteristics of ground objects by MRF model.Finally,spatial correlation constraint based on MRF model and NMF standard objective function were combined in the form of altemating iteration to estimate endmember spectrum and abundance of hyperspectral image.Theoretical analysis and experimental results indicated that,the endmember decomposition precision of SCNMF is 10.6%higher than that of Minimum Volume Constrained NMF(MVC-NMF),12.3%higher than that of Piecewise Smoothness NMF with Sparseness Constraints(PSNMFSC),14.1%higher than that of NMF with Alternating Projected Subgradients(APS-NMF); the abundance decomposition precision of SCNMF is 14.4%higher than that of MVC-NMF,15.9%higher than that of PSNMFSC,15.3%higher than that of APS-NMF.The proposed SCNMF can remedy NMFs deficiency in describing spatial correlation characteristics,and decrease spatial energy distribution error.
Bo Yuan
College of Computer and lnformation Engineering,Nanyang lnstitute of Technology,Nanyang Henan 473004,China
国际会议
上海
英文
1-7
2018-10-12（万方平台首次上网日期，不代表论文的发表时间）