Optics and Precision Engineering, Volume. 30, Issue 14, 1657(2022)

Multi-graph regularized multi-kernel nonnegative matrix factorization for hyperspectral image unmixing

Jing LIU1,*... Kangxin LI1, You ZHANG1 and Yi LIU2 |Show fewer author(s)
Author Affiliations
  • 1School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an702, China
  • 2School of Electronic Engineering, Xidian University, Xi'an710071, China
  • show less
    Figures & Tables(14)
    Endmember spectra randomly generated by spectrum library
    Abundance graphs of MGMKNMF algorithm on Cuprite
    Abundance graphs of each algorithm on Jasper Ridge
    • Table 1. [in Chinese]

      View table
      View in Article

      Table 1. [in Chinese]

      MGMKNMF解混算法

      输入:原始高光谱数据XL个核矩阵{K1,,Kl,,KL}M个图拉普拉斯矩阵{L1,,Lm,,LM},最大迭代次数T

      Step1.初始化矩阵F0S0

      Step2.初始化核权重变量τl0=1/L,l=1,,L,初始化图权重γm0=1/M,m=1,,M

      for t=1 to T do

      通过式(7)更新图Gτt和相应的拉普拉斯矩阵Lτt

      通过式(11)和式(12)更新FtSt

      通过式(13)式(14)更新核权重τt和图权重γt

      end

      输出: F=Ft-1, S=St-1, τ=τt-1, γ=γt-1

    • Table 1. SAD value of each algorithm of HAPKE model under different SNR

      View table
      View in Article

      Table 1. SAD value of each algorithm of HAPKE model under different SNR

      SNR/dBNMFGNMFnpKNMFKSNMFKDPMGKNMFMGMKNMF
      100.494 80.456 70.170 20.169 30.167 10.190 10.213 3
      200.382 90.309 40.164 80.160 40.154 30.157 60.154 1
      300.341 70.296 00.162 10.153 10.148 20.129 4.0.124 8
      400.308 40.251 90.159 60.149 20.142 70.146 60.137 5
    • Table 2. SAD value of each algorithm of GBM model under different SNR

      View table
      View in Article

      Table 2. SAD value of each algorithm of GBM model under different SNR

      SNR/dBNMFGNMFnpKNMFKSNMFKDPMGKNMFMGMKNMF
      100.401 10.283 90.180 30.172 30.171 40.169 30.258 8
      200.395 90.236 40.174 20.166 80.164 30.145 50.121 9
      300.341 30.214 20.168 10.161 10.160 80.139 00.113 1
      400.300 10.199 80.161 30.159 10.157 70.149 90.145 6
    • Table 3. RMSE value of each algorithm of HAPKE model under different SNR

      View table
      View in Article

      Table 3. RMSE value of each algorithm of HAPKE model under different SNR

      SNR/dBNMFGNMFnpKNMFKSNMFKDPMGKNMFMGMKNMF
      100.185 00.186 90.079 20.068 90.067 20.099 00.091 0
      200.162 40.183 90.076 90.063 70.061 10.061 80.060 1
      300.149 50.180 10.071 30.058 70.058 00.052 30.049 0
      400.129 90.178 30.066 20.052 30.051 80.051 50.050 6
    • Table 4. RMSE value of each algorithm of GBM model under different SNR

      View table
      View in Article

      Table 4. RMSE value of each algorithm of GBM model under different SNR

      SNR/dBNMFGNMFnpKNMFKSNMFKDPMKGNMFMGMKNMF
      100.321 70.290 60.081 70.071 10.070 80.079 00.112 3
      200.307 40.253 10.079 40.062 40.061 80.060 60.059 7
      300.292 30.233 70.073 60.060 30.059 30.052 20.050 1
      400.258 60.201 20.069 10.058 40.058 00.057 70.057 0
    • Table 5. SAD value of each algorithm of HAPKE model under different number of endmembers

      View table
      View in Article

      Table 5. SAD value of each algorithm of HAPKE model under different number of endmembers

      EndmemberNMFGNMFnpKNMFKSNMFKDPMGKNMFMGMKNMF
      P=60.308 40.281 70.159 60.149 00.142 20.145 70.141 1
      P=50.251 00.218 70.124 00.118 20.107 70.096 80.081 4
      P=40.198 50.195 40.100 40.089 80.081 20.080 20.078 9
      P=30.172 90.143 80.073 50.068 20.060 00.044 30.034 3
    • Table 6. SAD value of each algorithm of GBM model under different number of endmembers

      View table
      View in Article

      Table 6. SAD value of each algorithm of GBM model under different number of endmembers

      EndmemberNMFGNMFnpKNMFKSNMFKDPMGKNMFMGMKNMF
      P=60.271 10.278 90.169 50.159 80.158 00.148 30.147 1
      P=50.268 30.241 90.125 10.121 50.113 60.100 20.095 1
      P=40.209 20.202 10.091 40.085 40.081 70.070 00.063 2
      P=30.182 00.143 50.067 90.060 50.057 20.042 30.029 9
    • Table 7. RMSE value of each algorithm of HAPKE model under different number of endmembers

      View table
      View in Article

      Table 7. RMSE value of each algorithm of HAPKE model under different number of endmembers

      EndmemberNMFGNMFnpKNMFKSNMFKDPMGKNMFMGMKNMF
      P=60.189 90.176 40.084 10.068 10.066 90.060 30.059 4
      P=50.175 30.162 10.078 30.062 30.061 10.060 10.056 3
      P=40.151 50.160 10.074 50.058 90.057 40.054 70.052 3
      P=30.203 00.157 70.066 20.055 30.051 80.051 40.050 8
    • Table 8. RMSE value of each algorithm of GBM model under different number of endmembers

      View table
      View in Article

      Table 8. RMSE value of each algorithm of GBM model under different number of endmembers

      EndmemberNMFGNMFnpKNMFKSNMFKDPMGKNMFMGMKNMF
      P=60.227 20.200 10.086 30.079 20.077 50.060 10.061 0
      P=50.180 40.173 40.081 20.068 30.066 20,0 6220.060 3
      P=40.153 40.149 70.079 90.065 10.064 30.064 00.063 3
      P=30.119 60.139 60.069 10.058 40.058 00.041 20.035 4
    • Table 9. SAD values of Cuprite data by different algorithms

      View table
      View in Article

      Table 9. SAD values of Cuprite data by different algorithms

      ItemNMFGNMFnpKNMFKSNMFKDPMGKNMFMGMKNMF
      Alunite0.297 90.243 40.063 20.085 90.062 90.078 70.069 5
      Andradite0.383 90.262 60.079 50.101 40.204 50.066 00.079 4
      Buddingtonite0.367 20.266 60.079 60.073 70.116 10.088 50.119 6
      Dumortierite0.283 90.574 30.156 20.071 70.071 20.084 80.082 9
      Kaolinite_10.235 90.336 20.080 20.061 30.081 30.082 10.087 5
      Kaolinite_20.309 50.344 30.262 20.189 00.114 00.056 50.073 3
      Muscovite0.379 40.318 30.113 70.052 10.147 20.102 40.104 1
      Montmorillonite0.473 10.355 70.131 00.140 20.107 00.054 50.055 6
      Nontronite0.451 60.451 60.129 30.064 70.078 80.121 40.104 3
      Pyrope0.292 70.455 00.054 10.142 00.074 50.117 80.065 5
      Sphene0.282 10.287 90.091 30.279 80.097 60.201 50.197 1
      Chalcedony0.318 60.318 60.213 20.149 50.132 70.071 50.065 9
      Average0.339 60.337 10.120 80.117 60.107 80.093 80.092 1
    • Table 10. SAD values of Jasper Ridge data by different algorithms

      View table
      View in Article

      Table 10. SAD values of Jasper Ridge data by different algorithms

      ItemNMFGNMFnpKNMFKSNMFKDPMGKNMFMGMKNMF
      Tree0.267 70.238 10.139 70.144 60.095 30.079 30.096 4
      Water0.319 20.338 80.118 60.114 30.102 50.091 10.120 3
      Soil0.428 90.081 90.131 70.105 30.123 70.124 20.097 1
      Road0.410 80.309 30.127 20.094 20.086 90.097 40.074 1
      Average0.356 60.242 00.129 30.114 60.102 10.098 00.097 0
    Tools

    Get Citation

    Copy Citation Text

    Jing LIU, Kangxin LI, You ZHANG, Yi LIU. Multi-graph regularized multi-kernel nonnegative matrix factorization for hyperspectral image unmixing[J]. Optics and Precision Engineering, 2022, 30(14): 1657

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Modern Applied Optics

    Received: Apr. 22, 2022

    Accepted: --

    Published Online: Sep. 6, 2022

    The Author Email: LIU Jing (zyhalj1975@163.com)

    DOI:10.37188/OPE.20223014.1657

    Topics