Multiple Endmember Hyperspectral Sparse Unmixing Based on Improved OMP Algorithm  

作　　者：Chunhui Zhao Haifeng Zhu Shiling Cui Bin Qi 

机构地区：[1]College of Information and Communication Engineering,Harbin Engineering University

出　　处：《Journal of Harbin Institute of Technology(New Series)》2015年第5期97-104,共8页哈尔滨工业大学学报（英文版）

基　　金：Sponsored by the National Natural Science Foundation of China(Grant No.61405041,61571145);the Key Program of Heilongjiang Natural Science Foundation(Grant No.ZD201216);the Program Excellent Academic Leaders of Harbin(Grant No.RC2013XK009003);the China Postdoctoral Science Foundation(Grant No.2014M551221);the Heilongjiang Postdoctoral Science Found(Grant No.LBH-Z13057)

摘　　要：In conventional linear spectral mixture analysis model,a class is represented by a single endmember.However,the intra-class spectral variability is usually very large,which makes it difficult to represent a class,and in this case,it leads to incorrect unmixing results. Some proposed algorithms play a positive role in overcoming the endmember variability,but there are shortcomings on computation intensive,unsatisfactory unmixing results and so on. Recently,sparse regression has been applied to unmixing,assuming each mixed pixel can be expressed as a linear combination of only a few spectra in a spectral library. It is essentially the same as multiple endmember spectral unmixing. OMP( orthogonal matching pursuit),a sparse reconstruction algorithm,has advantages of simple structure and high efficiency. However,it does not take into account the constraints of abundance non-negativity and abundance sum-to-one( ANC and ASC),leading to undesirable unmixing results. In order to solve these issues,this paper presents an improved OMP algorithm( fully constraint OMP,FOMP) for multiple endmember hyperspectral sparse unmixing. The proposed algorithm overcomes the shortcomings of OMP,and on the other hand,it solves the problem of endmember variability.The ANC and ASC constraints are firstly added into the OMP algorithm,and then the endmember set is refined by the relative increase in root-mean-square-error( RMSE) to avoid over-fitting,finally pixels are unmixed by their optimal endmember set. The simulated and real hyperspectral data experiments show that FOPM unmixing results are ideally comparable and abundance RMSE reduces much lower than OMP and simple spectral mixture analysis( s SMA),and has a strong anti-noise performance. It proves that multiple endmember spectral mixture analysis is more reasonable.In conventional linear spectral mixture analysis model,a class is represented by a single endmember.However,the intra-class spectral variability is usually very large,which makes it difficult to represent a class,and in this case,it leads to incorrect unmixing results. Some proposed algorithms play a positive role in overcoming the endmember variability,but there are shortcomings on computation intensive,unsatisfactory unmixing results and so on. Recently,sparse regression has been applied to unmixing,assuming each mixed pixel can be expressed as a linear combination of only a few spectra in a spectral library. It is essentially the same as multiple endmember spectral unmixing. OMP（ orthogonal matching pursuit）,a sparse reconstruction algorithm,has advantages of simple structure and high efficiency. However,it does not take into account the constraints of abundance non-negativity and abundance sum-to-one（ ANC and ASC）,leading to undesirable unmixing results. In order to solve these issues,this paper presents an improved OMP algorithm（ fully constraint OMP,FOMP） for multiple endmember hyperspectral sparse unmixing. The proposed algorithm overcomes the shortcomings of OMP,and on the other hand,it solves the problem of endmember variability.The ANC and ASC constraints are firstly added into the OMP algorithm,and then the endmember set is refined by the relative increase in root-mean-square-error（ RMSE） to avoid over-fitting,finally pixels are unmixed by their optimal endmember set. The simulated and real hyperspectral data experiments show that FOPM unmixing results are ideally comparable and abundance RMSE reduces much lower than OMP and simple spectral mixture analysis（ s SMA）,and has a strong anti-noise performance. It proves that multiple endmember spectral mixture analysis is more reasonable.

关 键 词：HYPERSPECTRAL image SPARSE representation MULTIPLE ENDMEMBER spectral UNMIXING OMP ANC and ASC 

分 类 号：TN911.73[电子电信—通信与信息系统]